├── .gitmodules ├── Dockerfile ├── README.md ├── experiment_metrics_speed.py ├── experiment_speed_11_17.py ├── figures_icassp_final ├── experiment2_evaltime.pdf ├── experiment2_evaltime.png ├── experiment2_metrics.pdf ├── experiment2_metrics.png ├── experiment2_runtime.pdf ├── experiment2_runtime.png ├── rt60_hist.pdf └── rt60_hist.png ├── get_data.py ├── make_figures.py ├── room_builder.py └── sim_results ├── experiment_metrics_speed_results.json └── experiment_speed_11_17_results.json /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "piva"] 2 | path = piva 3 | url = https://github.com/fakufaku/piva.git 4 | [submodule "bsseval"] 5 | path = bsseval 6 | url = https://github.com/fakufaku/bsseval.git 7 | branch = efficient_permutation 8 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # We will use Ubuntu for our image 2 | FROM ubuntu:latest 3 | 4 | # Updating Ubuntu packages 5 | RUN apt-get update && yes|apt-get upgrade 6 | RUN apt-get install -y emacs neovim git gcc g++ 7 | 8 | # Adding wget and bzip2 9 | RUN apt-get install -y wget bzip2 10 | 11 | # Add sudo 12 | RUN apt-get -y install sudo 13 | 14 | # Add user ubuntu with no password, add to sudo group 15 | RUN adduser --disabled-password --gecos '' ubuntu 16 | RUN adduser ubuntu sudo 17 | RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers 18 | USER ubuntu 19 | WORKDIR /home/ubuntu/ 20 | RUN chmod a+rwx /home/ubuntu/ 21 | #RUN echo `pwd` 22 | 23 | # Anaconda installing 24 | RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh 25 | RUN bash Miniconda3-latest-Linux-x86_64.sh -b 26 | RUN rm Miniconda3-latest-Linux-x86_64.sh 27 | 28 | # Set path to conda 29 | #ENV PATH /root/anaconda3/bin:$PATH 30 | ENV PATH /home/ubuntu/miniconda3/bin:$PATH 31 | 32 | # Updating Anaconda packages 33 | RUN conda update conda 34 | RUN conda update --all 35 | 36 | # Get package 37 | RUN git clone --recursive https://github.com/onolab-tmu/code_2020ICASSP_iss.git 38 | ENV PYTHONPATH=/home/ubuntu/code_2020ICASSP_iss:$PYTHONPATH 39 | 40 | # Install environment 41 | RUN cd code_2020ICASSP_iss 42 | RUN conda env create -f piva/environment.yml 43 | RUN conda activate piva 44 | RUN cd piva 45 | RUN python setup.py build_ext --inplace 46 | RUN cd .. 47 | 48 | # Run Jupytewr notebook as Docker main process 49 | CMD [ "sh", "--login", "-i" ] 50 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Fast and stable blind source separation with rank-1 updates 2 | =========================================================== 3 | 4 | Abstract 5 | -------- 6 | 7 | We propose a new algorithm for the blind source separation of acoustic 8 | sources. This algorithm is an alternative to the popular auxiliary function 9 | based independent vector analysis using iterative projection (AuxIVA-IP). It 10 | optimizes the same cost function, but instead of alternate updates of the rows 11 | of the demixing matrix, we propose a sequence of rank-1 updates. Remarkably, 12 | and unlike the previous method, the resulting updates do not require matrix 13 | inversion. Moreover, their computational complexity is quadratic in the 14 | number of microphones, rather than cubic in AuxIVA-IP. In addition, we show 15 | that the new method can be derived as alternate updates of the steering 16 | vectors of sources. Accordingly, we name the method iterative source steering 17 | (AuxIVA-ISS). Finally, we confirm in simulated experiments that the proposed 18 | algorithm separates sources just as well as AuxIVA-IP, at a lower computational 19 | cost. 20 | 21 | Authors 22 | ------- 23 | 24 | * [Robin Scheibler](http://www.robinscheibler.org) 25 | * [Nobutaka Ono](http://www.comp.sd.tmu.ac.jp/onolab/index-e.html) 26 | 27 | Install 28 | ------- 29 | 30 | We use [anaconda](https://www.anaconda.com/distribution/) to setup the Python environment. 31 | 32 | git clone --recursive 33 | cd piva 34 | conda env create -f environment.yml 35 | conda activate piva 36 | python setup.py build_ext --inplace 37 | cd .. 38 | 39 | Run Experiments 40 | --------------- 41 | 42 | The two experiments presented in the paper can be run by the following steps. 43 | This produces two files `./experiment_metrics_speed_results.json` `./experiment_speed_11_17_results.json` that are later used to produce the plots. 44 | 45 | conda activate piva 46 | 47 | # Run the simulations 48 | python ./experiment_metrics_speed.py 49 | python ./experiment_speed_11_17.py 50 | 51 | # Plot the results 52 | python ./make_figures.py ./experiment_metrics_speed_results.json ./experiment_speed_11_17_results.json 53 | 54 | The two simulation output data files produced for the figures in the paper were kept in the `sim_results` folder. 55 | 56 | License 57 | ------- 58 | 59 | The code in this repository is released under the [MIT license](https://opensource.org/licenses/MIT). 60 | -------------------------------------------------------------------------------- /experiment_metrics_speed.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Robin Scheibler 2 | # 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy 4 | # of this software and associated documentation files (the "Software"), to deal 5 | # in the Software without restriction, including without limitation the rights 6 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | # copies of the Software, and to permit persons to whom the Software is 8 | # furnished to do so, subject to the following conditions: 9 | # 10 | # The above copyright notice and this permission notice shall be included in 11 | # all copies or substantial portions of the Software. 12 | # 13 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | # SOFTWARE. 20 | import argparse 21 | import json 22 | import os 23 | import time 24 | 25 | import numpy as np 26 | import pyroomacoustics as pra 27 | 28 | from bsseval.bsseval.metrics import bss_eval 29 | from get_data import samples_dir 30 | from piva.piva import auxiva, auxiva_iss 31 | from room_builder import random_room_builder 32 | from samples.generate_samples import sampling, wav_read_center 33 | 34 | # Simulation parameters 35 | config = { 36 | "n_repeat": 100, 37 | "seed": 840808, 38 | "snr": 30, 39 | "n_sources_list": [2, 3, 4, 6, 8, 10], 40 | "algorithms": { 41 | "auxiva_laplace": {"name": "auxiva", "kwargs": {"model": "laplace"}}, 42 | "auiva_iss_laplace": {"name": "auxiva_iss", "kwargs": {"model": "laplace"}}, 43 | }, 44 | "separation_params": {"ref_mic": 0, "n_iter_multiplier": 10}, 45 | "stft_params": {"n_fft": 4096, "hop": 2048, "win": "hamming"}, 46 | "samples_metadata": os.path.join(samples_dir, "metadata.json"), 47 | "room_params": { 48 | "mic_delta": 0.02, 49 | "fs": 16000, 50 | "t60_interval": [0.150, 0.350], 51 | "room_width_interval": [6, 10], 52 | "room_height_interval": [2.8, 4.5], 53 | "source_zone_height": [1.0, 2.0], 54 | "guard_zone_width": 0.5, 55 | }, 56 | "output_file": "experiment_metrics_speed_results.json", 57 | } 58 | 59 | # Placeholder to hold all the results 60 | sim_results = {"config": config, "room_info": [], "data": []} 61 | 62 | 63 | if __name__ == "__main__": 64 | 65 | np.random.seed(config["seed"]) 66 | 67 | max_sources = np.max(config["n_sources_list"]) 68 | audio_files = sampling(config["n_repeat"], max_sources, config["samples_metadata"]) 69 | ref_mic = config["separation_params"]["ref_mic"] 70 | 71 | for room_id, file_list in enumerate(audio_files): 72 | print(room_id) 73 | 74 | audio = wav_read_center(file_list) 75 | 76 | # Get a random room and simulate 77 | room, rt60 = random_room_builder(audio, max_sources, **config["room_params"]) 78 | premix = room.simulate(return_premix=True) 79 | sim_results["room_info"].append( 80 | { 81 | "dim": room.shoebox_dim.tolist(), 82 | "rt60": rt60, 83 | "id": room_id, 84 | "samples": file_list, 85 | "n_samples": premix.shape[2], 86 | "fs": room.fs, 87 | } 88 | ) 89 | 90 | # normalize all sources at the reference mic 91 | premix /= np.std(premix[:, ref_mic, :], axis=1, keepdims=True) 92 | 93 | for n_sources in config["n_sources_list"]: 94 | 95 | # Do the mix and add noise 96 | mix = np.sum(premix[:n_sources, :n_sources, :], axis=0) 97 | noise_std = 10 ** (-config["snr"] / 20) * np.std(mix[ref_mic, :]) 98 | mix += noise_std * np.random.randn(*mix.shape) 99 | ref = premix[:n_sources, ref_mic, :] 100 | 101 | # Measure SDR/SIR at input 102 | sdr0, isr0, sir0, sar0, perm0 = bss_eval( 103 | ref[:, :, None], 104 | mix[:, :, None], 105 | compute_permutation=True, 106 | window=ref.shape[1], 107 | ) 108 | 109 | # STFT 110 | n_fft = config["stft_params"]["n_fft"] 111 | hop = config["stft_params"]["hop"] 112 | if config["stft_params"]["win"] == "hamming": 113 | win_a = pra.hamming(n_fft) 114 | else: 115 | raise ValueError("Undefined window function") 116 | win_s = pra.transform.compute_synthesis_window(win_a, hop) 117 | 118 | X = pra.transform.analysis(mix.T, n_fft, hop, win=win_a) 119 | 120 | n_iter = config["separation_params"]["n_iter_multiplier"] * n_sources 121 | 122 | # Separation 123 | for algo, details in config["algorithms"].items(): 124 | 125 | t1 = time.perf_counter() 126 | 127 | if details["name"] == "auxiva": 128 | Y = auxiva( 129 | X, 130 | proj_back=False, 131 | n_iter=n_iter, 132 | backend="cpp", 133 | **details["kwargs"], 134 | ) 135 | elif details["name"] == "auxiva_iss": 136 | Y = auxiva_iss( 137 | X, 138 | proj_back=False, 139 | n_iter=n_iter, 140 | backend="cpp", 141 | **details["kwargs"], 142 | ) 143 | 144 | t2 = time.perf_counter() 145 | sep_time = t2 - t1 146 | 147 | # projection back 148 | z = pra.bss.projection_back(Y, X[:, :, ref_mic]) 149 | Y = Y * np.conj(z[None, :, :]) 150 | 151 | # Inverse STFT 152 | y = pra.transform.synthesis(Y, n_fft, hop, win=win_s) 153 | y = y[n_fft - hop :, :].T 154 | 155 | # metrics 156 | t1 = time.perf_counter() 157 | 158 | m = np.minimum(y.shape[1], ref.shape[1]) 159 | sdr, isr, sir, sar, perm = bss_eval( 160 | ref[:, :m, None], y[:, :m, None], compute_permutation=True, window=m 161 | ) 162 | 163 | t2 = time.perf_counter() 164 | eval_time = t2 - t1 165 | 166 | # store the results 167 | sim_results["data"].append( 168 | { 169 | "algo": algo, 170 | "room_id": room_id, 171 | "n_sources": n_sources, 172 | "sdr_mix": sdr0.tolist(), 173 | "sir_mix": sir0.tolist(), 174 | "sdr_out": sdr.tolist(), 175 | "sir_out": sir.tolist(), 176 | "runtime": sep_time, 177 | "evaltime": eval_time, 178 | } 179 | ) 180 | 181 | # compute time per second of signal and iteration 182 | t_signal = mix.shape[1] / room.fs 183 | sep_time_unit_ms = 1000 * sep_time / t_signal / n_iter 184 | 185 | print( 186 | f"{room_id} {n_sources} {algo} {np.mean(sdr):.2f} " 187 | f"{sep_time_unit_ms:.3f} [ms / s / iteration] (Total: " 188 | f"{sep_time:.3f}) {eval_time:.3f}" 189 | ) 190 | 191 | # Save to file regularly 192 | with open(config["output_file"], "w") as f: 193 | json.dump(sim_results, f) 194 | -------------------------------------------------------------------------------- /experiment_speed_11_17.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Robin Scheibler 2 | # 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy 4 | # of this software and associated documentation files (the "Software"), to deal 5 | # in the Software without restriction, including without limitation the rights 6 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | # copies of the Software, and to permit persons to whom the Software is 8 | # furnished to do so, subject to the following conditions: 9 | # 10 | # The above copyright notice and this permission notice shall be included in 11 | # all copies or substantial portions of the Software. 12 | # 13 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | # SOFTWARE. 20 | import argparse 21 | import json 22 | import os 23 | import time 24 | 25 | import numpy as np 26 | import pyroomacoustics as pra 27 | 28 | from bsseval.bsseval.metrics import bss_eval 29 | from get_data import samples_dir 30 | from piva.piva import auxiva, auxiva_iss 31 | from room_builder import random_room_builder 32 | from samples.generate_samples import sampling, wav_read_center 33 | 34 | # Simulation parameters 35 | config = { 36 | "n_repeat": 100, 37 | "seed": 840808, 38 | "snr": 30, 39 | "n_sources_list": [11, 12, 13, 14, 15, 16, 17], 40 | "algorithms": { 41 | "auxiva_laplace": {"name": "auxiva", "kwargs": {"model": "laplace"}}, 42 | "auxiva_iss_laplace": {"name": "auxiva_iss", "kwargs": {"model": "laplace"}}, 43 | }, 44 | "separation_params": {"ref_mic": 0, "n_iter_multiplier": 10}, 45 | "stft_params": {"n_fft": 4096, "hop": 2048, "win": "hamming"}, 46 | "samples_metadata": os.path.join(samples_dir, "metadata.json"), 47 | "room_params": { 48 | "mic_delta": 0.02, 49 | "fs": 16000, 50 | "t60_interval": [0.150, 0.350], 51 | "room_width_interval": [6, 10], 52 | "room_height_interval": [2.8, 4.5], 53 | "source_zone_height": [1.0, 2.0], 54 | "guard_zone_width": 0.5, 55 | }, 56 | "output_file": "experiment_speed_11_17_results.json", 57 | } 58 | 59 | # Placeholder to hold all the results 60 | sim_results = {"config": config, "room_info": [], "data": []} 61 | 62 | 63 | if __name__ == "__main__": 64 | 65 | np.random.seed(config["seed"]) 66 | 67 | min_sources = np.min(config["n_sources_list"]) 68 | max_sources = np.max(config["n_sources_list"]) 69 | audio_files = sampling(config["n_repeat"], min_sources, config["samples_metadata"]) 70 | ref_mic = config["separation_params"]["ref_mic"] 71 | 72 | for room_id, file_list in enumerate(audio_files): 73 | 74 | audio = wav_read_center(file_list) 75 | 76 | # Get a random room and simulate 77 | room, rt60 = random_room_builder(audio, max_sources, **config["room_params"]) 78 | premix = room.simulate(return_premix=True) 79 | sim_results["room_info"].append( 80 | { 81 | "dim": room.shoebox_dim.tolist(), 82 | "rt60": rt60, 83 | "id": room_id, 84 | "samples": file_list, 85 | "n_samples": premix.shape[2], 86 | "fs": room.fs, 87 | } 88 | ) 89 | 90 | # normalize all sources at the reference mic 91 | premix /= np.std(premix[:, ref_mic, None, :], axis=2, keepdims=True) 92 | 93 | for n_sources in config["n_sources_list"]: 94 | 95 | # Do the mix and add noise 96 | mix = np.sum(premix[:, :n_sources, :], axis=0) 97 | noise_std = 10 ** (-config["snr"] / 20) * np.std(mix[ref_mic, :]) 98 | mix += noise_std * np.random.randn(*mix.shape) 99 | ref = premix[:n_sources, ref_mic, :] 100 | 101 | # STFT 102 | n_fft = config["stft_params"]["n_fft"] 103 | hop = config["stft_params"]["hop"] 104 | if config["stft_params"]["win"] == "hamming": 105 | win_a = pra.hamming(n_fft) 106 | else: 107 | raise ValueError("Undefined window function") 108 | win_s = pra.transform.compute_synthesis_window(win_a, hop) 109 | 110 | X = pra.transform.analysis(mix.T, n_fft, hop, win=win_a) 111 | 112 | n_iter = config["separation_params"]["n_iter_multiplier"] * n_sources 113 | 114 | # Separation 115 | for algo, details in config["algorithms"].items(): 116 | 117 | t1 = time.perf_counter() 118 | 119 | if details["name"] == "auxiva": 120 | Y = auxiva( 121 | X, 122 | proj_back=False, 123 | n_iter=n_iter, 124 | backend="cpp", 125 | **details["kwargs"], 126 | ) 127 | elif details["name"] == "auxiva_iss": 128 | Y = auxiva_iss( 129 | X, 130 | proj_back=False, 131 | n_iter=n_iter, 132 | backend="cpp", 133 | **details["kwargs"], 134 | ) 135 | 136 | t2 = time.perf_counter() 137 | sep_time = t2 - t1 138 | 139 | # store the results 140 | sim_results["data"].append( 141 | { 142 | "algo": algo, 143 | "room_id": room_id, 144 | "n_sources": n_sources, 145 | "runtime": sep_time, 146 | } 147 | ) 148 | 149 | # compute time per second of signal and iteration 150 | t_signal = mix.shape[1] / room.fs 151 | sep_time_unit_ms = 1000 * sep_time / t_signal / n_iter 152 | 153 | print( 154 | f"{room_id} {n_sources} {algo} {sep_time_unit_ms:.3f} " 155 | f"[ms*iteration] (Total: {sep_time:.3f})" 156 | ) 157 | 158 | # Save to file regularly 159 | with open(config["output_file"], "w") as f: 160 | json.dump(sim_results, f) 161 | -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_evaltime.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_evaltime.pdf -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_evaltime.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_evaltime.png -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_metrics.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_metrics.pdf -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_metrics.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_metrics.png -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_runtime.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_runtime.pdf -------------------------------------------------------------------------------- /figures_icassp_final/experiment2_runtime.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/experiment2_runtime.png -------------------------------------------------------------------------------- /figures_icassp_final/rt60_hist.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/rt60_hist.pdf -------------------------------------------------------------------------------- /figures_icassp_final/rt60_hist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/onolab-tmu/code_2020ICASSP_iss/01bbed90bc56e71d58e1764f14a5febce150cb76/figures_icassp_final/rt60_hist.png -------------------------------------------------------------------------------- /get_data.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019 Robin Scheibler 2 | # 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy 4 | # of this software and associated documentation files (the "Software"), to deal 5 | # in the Software without restriction, including without limitation the rights 6 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | # copies of the Software, and to permit persons to whom the Software is 8 | # furnished to do so, subject to the following conditions: 9 | # 10 | # The above copyright notice and this permission notice shall be included in all 11 | # copies or substantial portions of the Software. 12 | # 13 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | # SOFTWARE. 20 | """ 21 | This script can be used to download the data used in the experiments. 22 | """ 23 | import os 24 | from pyroomacoustics.datasets.utils import download_uncompress 25 | 26 | url_data = "https://zenodo.org/record/3066489/files/cmu_arctic_concat15.tar.gz" 27 | temp_dir = "./temp" 28 | samples_dir = "./samples" 29 | 30 | 31 | def get_data(): 32 | if os.path.exists(samples_dir): 33 | print( 34 | f"The samples directory {samples_dir} seems to " 35 | f"exist already. Delete if re-download is needed." 36 | ) 37 | else: 38 | print("Downloading the samples... ", end="", flush=True) 39 | download_uncompress(url_data, temp_dir) 40 | # change the directory name to the desired one 41 | dl_dir = os.listdir(temp_dir)[0] 42 | os.rename(os.path.join(temp_dir, dl_dir), samples_dir) 43 | os.rmdir(temp_dir) 44 | print("done.") 45 | 46 | 47 | get_data() 48 | 49 | if __name__ == "__main__": 50 | pass 51 | -------------------------------------------------------------------------------- /make_figures.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Robin Scheibler 2 | # 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy 4 | # of this software and associated documentation files (the "Software"), to deal 5 | # in the Software without restriction, including without limitation the rights 6 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | # copies of the Software, and to permit persons to whom the Software is 8 | # furnished to do so, subject to the following conditions: 9 | # 10 | # The above copyright notice and this permission notice shall be included in 11 | # all copies or substantial portions of the Software. 12 | # 13 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | # SOFTWARE. 20 | import argparse 21 | import json 22 | import os 23 | 24 | import matplotlib.pyplot as plt 25 | import numpy as np 26 | import pandas as pd 27 | import seaborn as sns 28 | 29 | if __name__ == "__main__": 30 | 31 | parser = argparse.ArgumentParser( 32 | description="Experiment where one source moves in the middle of the experiment" 33 | ) 34 | parser.add_argument( 35 | "files", metavar="FILE", nargs="+", type=str, help="Result files" 36 | ) 37 | parser.add_argument( 38 | "-o", "--out", type=str, default="figures", help="Directory to save figures" 39 | ) 40 | args = parser.parse_args() 41 | 42 | # Read in the data 43 | all_records = [] 44 | room_info = None 45 | config = None 46 | for fn in args.files: 47 | print(fn) 48 | with open(fn, "r") as f: 49 | sim_results = json.load(f) 50 | all_records += sim_results["data"] 51 | 52 | if room_info is None: 53 | room_info = sim_results["room_info"] 54 | 55 | if config is None: 56 | config = sim_results["config"] 57 | 58 | # Compute the duration of all signals use 59 | t_signals = [r["n_samples"] / r["fs"] for r in room_info] 60 | rt60 = np.array([r["rt60"] for r in room_info]) 61 | 62 | # Create output directory if necessary 63 | if not os.path.exists(args.out): 64 | os.mkdir(args.out) 65 | 66 | algo_dict = { 67 | "auxiva_laplace": "AuxIVA-IP", 68 | "auxiva_iss_laplace": "AuxIVA-ISS (new)", 69 | "mixiva_laplace": "AuxIVA-ISS (new)", 70 | } 71 | 72 | df = pd.DataFrame( 73 | columns=[ 74 | "Algorithm", 75 | "Channels", 76 | "SDR Raw [dB]", 77 | "SIR Raw [dB]", 78 | "\u0394SDR [dB]", 79 | "\u0394SIR [dB]", 80 | "Runtime per Iteration [ms]", 81 | "Evaltime [s]", 82 | ] 83 | ) 84 | 85 | n_sources_perf = set() 86 | 87 | for record in all_records: 88 | 89 | if "sdr_mix" in record: 90 | sdr_mix = np.array(record["sdr_mix"]) 91 | sdr_out = np.array(record["sdr_out"]) 92 | sir_mix = np.array(record["sir_mix"]) 93 | sir_out = np.array(record["sir_out"]) 94 | 95 | sdr_raw = np.mean(sdr_out) 96 | sir_raw = np.mean(sir_out) 97 | d_sdr = np.mean(sdr_out - sdr_mix) 98 | d_sir = np.mean(sir_out - sir_mix) 99 | 100 | evaltime = record["evaltime"] 101 | 102 | # Mark this to be in the performance figure 103 | n_sources_perf.add(record["n_sources"]) 104 | 105 | else: 106 | sdr_raw = np.nan 107 | sir_raw = np.nan 108 | d_sdr = np.nan 109 | d_sir = np.nan 110 | evaltime = np.nan 111 | 112 | if "runtime" in record: 113 | ts = t_signals[record["room_id"]] 114 | n_iter = ( 115 | config["separation_params"]["n_iter_multiplier"] * record["n_sources"] 116 | ) 117 | runtime = record["runtime"] / ts / n_iter 118 | runtime *= 1000 # make milliseconds 119 | else: 120 | runtime = np.nan 121 | 122 | df = df.append( 123 | [ 124 | { 125 | "Algorithm": algo_dict[record["algo"]], 126 | "Channels": record["n_sources"], 127 | "SDR Raw [dB]": sdr_raw, 128 | "SIR Raw [dB]": sir_raw, 129 | "\u0394SDR [dB]": d_sdr, 130 | "\u0394SIR [dB]": d_sir, 131 | "Runtime per Iteration [ms]": runtime, 132 | "Evaltime [s]": evaltime, 133 | } 134 | ] 135 | ) 136 | 137 | # Reorganize the data in the dataframe for easier plotting with seaborn 138 | df_melt = df.melt(id_vars=["Algorithm", "Channels"], var_name="Metric") 139 | 140 | # Set the properties with seaborn 141 | sns.set( 142 | style="whitegrid", 143 | context="paper", 144 | font_scale=0.75, 145 | rc={ 146 | "figure.figsize": (3.35, 2), 147 | "lines.linewidth": 1.0, 148 | # 'font.family': 'sans-serif', 149 | # 'font.sans-serif': [u'Helvetica'], 150 | # 'text.usetex': False, 151 | }, 152 | ) 153 | 154 | # Figure SDR/SIR 155 | aspect = 0.9 156 | width = 3.35 157 | n_plot_row = 2 158 | height = (width / n_plot_row) / aspect 159 | 160 | fig = plt.figure() 161 | g1 = sns.catplot( 162 | kind="box", 163 | data=df_melt, 164 | x="Channels", 165 | y="value", 166 | order=sorted(n_sources_perf), 167 | col="Metric", 168 | col_order=["\u0394SDR [dB]", "\u0394SIR [dB]"], 169 | hue="Algorithm", 170 | aspect=aspect, 171 | height=height, 172 | whis=np.inf, 173 | legend_out=False, 174 | legend=False, 175 | ) 176 | g1.set_titles(col_template="{col_name}", row_template="") 177 | g1.set(ylim=[-3, 24]) 178 | # sns.despine(offset=10, trim=True) 179 | sns.despine(offset=10, trim=False, left=True, bottom=True) 180 | 181 | # g1.facet_axis(0, 0).set(clip_on=False) 182 | # g1.facet_axis(0, 1).set(clip_on=False) 183 | 184 | # left_ax = g1.facet_axis(0, 1) 185 | # leg = fig.legend( 186 | # [left_ax], 187 | leg = plt.legend( 188 | title="Algorithm", 189 | frameon=True, 190 | framealpha=0.85, 191 | # fontsize="x-small", 192 | loc="upper right", 193 | bbox_to_anchor=[1.08, 1.07], 194 | ) 195 | leg.get_frame().set_linewidth(0.2) 196 | all_artists = [leg] 197 | 198 | g1.facet_axis(0, 0).set_ylabel("Improvement [dB]") 199 | 200 | for ext in ["pdf", "png"]: 201 | fn = os.path.join(args.out, f"experiment2_metrics.{ext}") 202 | plt.savefig(fn, bbox_extra_artists=all_artists, bbox_inches="tight") 203 | plt.close() 204 | 205 | # Figure Runtime 206 | ax = sns.lineplot( 207 | data=df_melt[df_melt["Metric"] == "Runtime per Iteration [ms]"], 208 | x="Channels", 209 | y="value", 210 | hue="Algorithm", 211 | style="Algorithm", 212 | markers=True, 213 | dashes=False, 214 | ) 215 | leg = plt.legend() 216 | leg.get_frame().set_linewidth(0.2) 217 | ax.yaxis.grid("off") 218 | ax.grid(False, axis="x") 219 | # sns.despine(offset=10, trim=True) 220 | sns.despine(offset=10, trim=False, left=True, bottom=True) 221 | plt.ylabel("Runtime per Iteration [ms]") 222 | 223 | for ext in ["pdf", "png"]: 224 | fn = os.path.join(args.out, f"experiment2_runtime.{ext}") 225 | plt.savefig(fn, bbox_inches="tight") 226 | plt.close() 227 | 228 | # Figure for evaluation time (bss_eval) 229 | g2 = sns.catplot( 230 | kind="point", 231 | data=df_melt, 232 | x="Channels", 233 | y="value", 234 | col="Metric", 235 | col_order=["Evaltime [s]"], 236 | ) 237 | sns.despine(offset=10, trim=False, left=True, bottom=True) 238 | plt.tight_layout(pad=0.1) 239 | 240 | for ext in ["pdf", "png"]: 241 | fn = os.path.join(args.out, f"experiment2_evaltime.{ext}") 242 | plt.savefig(fn, bbox_inches="tight") 243 | plt.close() 244 | 245 | # Histogram of RT60 246 | plt.figure(figsize=(3.35, 1.8)) 247 | plt.figure(figsize=(2.5, 1.2)) 248 | plt.hist(rt60 * 1000.0) 249 | plt.xlabel("RT60 [ms]") 250 | plt.ylabel("Frequency") 251 | sns.despine(offset=10, trim=False, left=True, bottom=True) 252 | # plt.gca().xaxis.set_major_locator(MaxNLocator(integer=True)) 253 | # plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True)) 254 | for ext in ["pdf", "png"]: 255 | fig_fn = os.path.join(args.out, f"rt60_hist.{ext}") 256 | plt.savefig(fig_fn, bbox_inches="tight") 257 | plt.close() 258 | -------------------------------------------------------------------------------- /room_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Robin Scheibler 2 | # 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy 4 | # of this software and associated documentation files (the "Software"), to deal 5 | # in the Software without restriction, including without limitation the rights 6 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | # copies of the Software, and to permit persons to whom the Software is 8 | # furnished to do so, subject to the following conditions: 9 | # 10 | # The above copyright notice and this permission notice shall be included in 11 | # all copies or substantial portions of the Software. 12 | # 13 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 19 | # SOFTWARE. 20 | import itertools 21 | import math 22 | from typing import List, Optional, Tuple 23 | 24 | import numpy as np 25 | import pyroomacoustics as pra 26 | from numpy.random import rand 27 | 28 | 29 | def callback_noise_mixer( 30 | premix, sinr=0, diffuse_ratio=0, ref_mic=0, n_src=None, n_tgt=None, tgt_std=None, 31 | ): 32 | """ 33 | This callback function will rescale all the signals so that the SINR 34 | is fixed to a given value with a given ratio of diffuse noise 35 | """ 36 | 37 | if tgt_std is None: 38 | tgt_std = np.ones(n_tgt) 39 | 40 | # first normalize all separate recording to have unit power at microphone one 41 | p_mic_ref = np.std(premix[:, ref_mic, :], axis=1) 42 | premix /= p_mic_ref[:, None, None] 43 | premix[:n_tgt, :, :] *= tgt_std[:, None, None] 44 | 45 | # Total variance of noise components 46 | var_noise_tot = 10 ** (-sinr / 10) * np.sum(tgt_std ** 2) 47 | 48 | if n_tgt == n_src: 49 | diffuse_ratio = 0.0 50 | 51 | # compute noise variance 52 | sigma_n = np.sqrt((1 - diffuse_ratio) * var_noise_tot) 53 | 54 | if n_tgt < n_src: 55 | # now compute the power of interference signal needed to achieve desired SINR 56 | sigma_i = np.sqrt((diffuse_ratio / (n_src - n_tgt)) * var_noise_tot) 57 | premix[n_tgt:n_src, :, :] *= sigma_i 58 | 59 | # the background 60 | bg = np.sum(premix[n_tgt:n_src, :, :], axis=0) 61 | bg += sigma_n * np.random.randn(*premix.shape[1:]) 62 | 63 | # Mix down the recorded signals 64 | mix = np.sum(premix[:n_tgt, :, :], axis=0) + bg 65 | 66 | return mix 67 | 68 | 69 | def inv_sabine(t60, room_dim, c): 70 | """ 71 | given desired t60, (shoebox) room dimension and sound speed, 72 | computes the reflection coefficient (amplitude) and image source 73 | order needed. the speed of sound used is the package wide default 74 | (in :py:data:`pyroomacoustics.parameters.constants`). 75 | 76 | parameters 77 | ---------- 78 | t60: float 79 | desired t60 (time it takes to go from full amplitude to 60 db decay) in seconds 80 | room_dim: list of floats 81 | list of length 2 or 3 of the room side lengths 82 | c: float 83 | speed of sound 84 | 85 | returns 86 | ------- 87 | reflection: float 88 | the reflection coefficient (in amplitude domain, to be passed to 89 | room constructor) 90 | max_order: int 91 | the maximum image source order necessary to achieve the desired t60 92 | """ 93 | 94 | # finding image sources up to a maximum order creates a (possibly 3d) diamond 95 | # like pile of (reflected) rooms. now we need to find the image source model order 96 | # so that reflections at a distance of at least up to ``c * rt60`` are included. 97 | # one possibility is to find the largest sphere (or circle in 2d) that fits in the 98 | # diamond. this is what we are doing here. 99 | R = [] 100 | for l1, l2 in itertools.combinations(room_dim, 2): 101 | R.append(l1 * l2 / np.sqrt(l1 ** 2 + l2 ** 2)) 102 | 103 | V = np.prod(room_dim) # area (2d) or volume (3d) 104 | # "surface" computation is diff for 2d and 3d 105 | if len(room_dim) == 2: 106 | S = 2 * np.sum(room_dim) 107 | sab_coef = 12 # the sabine's coefficient needs to be adjusted in 2d 108 | elif len(room_dim) == 3: 109 | S = 2 * np.sum([l1 * l2 for l1, l2 in itertools.combinations(room_dim, 2)]) 110 | sab_coef = 24 111 | 112 | a2 = sab_coef * np.log(10) * V / (c * S * t60) # absorption in power (sabine) 113 | 114 | if a2 > 1.0: 115 | raise ValueError( 116 | "evaluation of parameters failed. room may be too large for required t60." 117 | ) 118 | 119 | reflection = np.sqrt(1 - a2) # convert to reflection coefficient 120 | 121 | max_order = math.ceil(c * t60 / np.min(R) - 1) 122 | 123 | return reflection, max_order 124 | 125 | 126 | def random_room_builder( 127 | source_signals: List[np.ndarray], 128 | n_mics: int, 129 | mic_delta: Optional[float] = None, 130 | fs: float = 16000, 131 | t60_interval: Tuple[float, float] = (0.150, 0.500), 132 | room_width_interval: Tuple[float, float] = (6, 10), 133 | room_height_interval: Tuple[float, float] = (2.8, 4.5), 134 | source_zone_height: Tuple[float, float] = [1.0, 2.0], 135 | guard_zone_width: float = 0.5, 136 | seed: Optional[int] = None, 137 | ): 138 | """ 139 | This function creates a random room within some parameters. 140 | 141 | The microphone array is circular with the distance between neighboring 142 | elements set to the maximal distance avoiding spatial aliasing. 143 | 144 | Parameters 145 | ---------- 146 | source_signals: list of numpy.ndarray 147 | A list of audio signals for each source 148 | n_mics: int 149 | The number of microphones in the microphone array 150 | mic_delta: float, optional 151 | The distance between neighboring microphones in the array 152 | fs: float, optional 153 | The sampling frequency for the simulation 154 | t60_interval: (float, float), optional 155 | An interval where to pick the reverberation time 156 | room_width_interval: (float, float), optional 157 | An interval where to pick the room horizontal length/width 158 | room_height_interval: (float, float), optional 159 | An interval where to pick the room vertical length 160 | source_zone_height: (float, float), optional 161 | The vertical interval where sources and microphones are allowed 162 | guard_zone_width: float 163 | The minimum distance between a vertical wall and a source/microphone 164 | 165 | Returns 166 | ------- 167 | ShoeBox object 168 | A randomly generated room according to the provided parameters 169 | float 170 | The measured T60 reverberation time of the room created 171 | """ 172 | 173 | # save current numpy RNG state and set a known seed 174 | if seed is not None: 175 | rng_state = np.random.get_state() 176 | np.random.seed(seed) 177 | 178 | n_sources = len(source_signals) 179 | 180 | # sanity checks 181 | assert source_zone_height[0] > 0 182 | assert source_zone_height[1] < room_height_interval[0] 183 | assert source_zone_height[0] <= source_zone_height[1] 184 | assert 2 * guard_zone_width < room_width_interval[1] - room_width_interval[0] 185 | 186 | def random_location( 187 | room_dim, n, ref_point=None, min_distance=None, max_distance=None 188 | ): 189 | """ Helper function to pick a location in the room """ 190 | 191 | width = room_dim[0] - 2 * guard_zone_width 192 | width_intercept = guard_zone_width 193 | 194 | depth = room_dim[1] - 2 * guard_zone_width 195 | depth_intercept = guard_zone_width 196 | 197 | height = np.diff(source_zone_height)[0] 198 | height_intercept = source_zone_height[0] 199 | 200 | locs = rand(3, n) 201 | locs[0, :] = locs[0, :] * width + width_intercept 202 | locs[1, :] = locs[1, :] * depth + depth_intercept 203 | locs[2, :] = locs[2, :] * height + height_intercept 204 | 205 | if ref_point is not None: 206 | # Check condition 207 | d = np.linalg.norm(locs - ref_point, axis=0) 208 | 209 | if min_distance is not None and max_distance is not None: 210 | redo = np.where(np.logical_or(d < min_distance, max_distance < d))[0] 211 | elif min_distance is not None: 212 | redo = np.where(d < min_distance)[0] 213 | elif max_distance is not None: 214 | redo = np.where(d > max_distance)[0] 215 | else: 216 | redo = [] 217 | 218 | # Recursively call this function on sources to redraw 219 | if len(redo) > 0: 220 | locs[:, redo] = random_location( 221 | room_dim, 222 | len(redo), 223 | ref_point=ref_point, 224 | min_distance=min_distance, 225 | max_distance=max_distance, 226 | ) 227 | 228 | return locs 229 | 230 | c = pra.constants.get("c") 231 | 232 | # Create the room 233 | # Sometimes the room dimension and required T60 are not compatible, then 234 | # we just try again with new random values 235 | retry = True 236 | while retry: 237 | try: 238 | room_dim = np.array( 239 | [ 240 | rand() * np.diff(room_width_interval)[0] + room_width_interval[0], 241 | rand() * np.diff(room_width_interval)[0] + room_width_interval[0], 242 | rand() * np.diff(room_height_interval)[0] + room_height_interval[0], 243 | ] 244 | ) 245 | t60 = rand() * np.diff(t60_interval)[0] + t60_interval[0] 246 | reflection, max_order = inv_sabine(t60, room_dim, c) 247 | retry = False 248 | except ValueError: 249 | pass 250 | # Create the room based on the random parameters 251 | room = pra.ShoeBox(room_dim, fs=fs, absorption=1 - reflection, max_order=max_order) 252 | 253 | # The critical distance 254 | # https://en.wikipedia.org/wiki/Critical_distance 255 | d_critical = 0.057 * np.sqrt(np.prod(room_dim) / t60) 256 | 257 | # default intermic distance is set according to nyquist criterion 258 | # i.e. 1/2 wavelength corresponding to fs / 2 at given speed of sound 259 | if mic_delta is None: 260 | mic_delta = 0.5 * (c / (0.5 * fs)) 261 | 262 | # the microphone array is uniformly circular with the distance between 263 | # neighboring elements of mic_delta 264 | mic_center = random_location(room_dim, 1) 265 | mic_rotation = rand() * 2 * np.pi 266 | mic_radius = 0.5 * mic_delta / np.sin(np.pi / n_mics) 267 | mic_array = pra.MicrophoneArray( 268 | np.vstack( 269 | ( 270 | pra.circular_2D_array( 271 | mic_center[:2, 0], n_mics, mic_rotation, mic_radius 272 | ), 273 | mic_center[2, 0] * np.ones(n_mics), 274 | ) 275 | ), 276 | room.fs, 277 | ) 278 | room.add_microphone_array(mic_array) 279 | 280 | # Now we will get the sources at random 281 | source_locs = [] 282 | 283 | # Choose the target location at least as far as the critical distance 284 | # Then the other sources, yet one further meter away 285 | target_source = random_location( 286 | room_dim, 287 | 1, 288 | ref_point=mic_center, 289 | min_distance=d_critical, 290 | max_distance=d_critical + 1, 291 | ) 292 | interferers = random_location( 293 | room_dim, n_sources - 1, ref_point=mic_center, min_distance=d_critical + 1 294 | ) 295 | source_locs = np.concatenate((target_source, interferers), axis=1) 296 | 297 | for s, signal in enumerate(source_signals): 298 | room.add_source(source_locs[:, s], signal=signal) 299 | 300 | # pre-compute the impulse responses 301 | room.compute_rir() 302 | t60_actual = pra.experimental.measure_rt60(room.rir[0][0], room.fs) 303 | 304 | # restore numpy RNG former state 305 | if seed is not None: 306 | np.random.set_state(rng_state) 307 | 308 | return room, t60_actual 309 | -------------------------------------------------------------------------------- /sim_results/experiment_speed_11_17_results.json: -------------------------------------------------------------------------------- 1 | {"config": {"n_repeat": 100, "seed": 840808, "snr": 30, "n_sources_list": [11, 12, 13, 14, 15, 16, 17], "algorithms": {"auxiva_laplace": {"name": "auxiva", "kwargs": {"model": "laplace"}}, "mixiva_laplace": {"name": "mixiva", "kwargs": {"model": "laplace"}}}, "separation_params": {"ref_mic": 0, "n_iter_multiplier": 10}, 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