├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── evaluation.py ├── experiments ├── __init__.py ├── models │ ├── __init__.py │ ├── han16.py │ ├── multilayer.py │ └── singlelayer.py └── settings.py ├── means ├── han16_mean.npy ├── multilayer_mean.npy └── singlelayer_mean.npy ├── metadata ├── classes.pkl ├── irmas_test_meta.csv └── irmas_train_meta.csv ├── preprocessing.py ├── requirements.txt ├── training.py └── weights ├── multilayer └── epoch.128-val_loss.0.866-fbeta.0.782.hdf5 └── singlelayer └── epoch.115-val_loss.0.902-fbeta.0.673.hdf5 /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Timbre Analysis of Music Audio Signals with Convolutional Neural Networks: Musical instrument recognition 2 | 3 | This repository contains code for musical instrument recognition experiments for the paper 4 | entitled "Timbre Analysis of Music Audio Signals with Convolutional Neural Networks". 5 | 6 | We provide the code for data preprocessing, training and evaluation of our approach. 7 | 8 | ## Dataset 9 | 10 | We use IRMAS dataset. Please, download the dataset from [MTG website] (www.mtg.upf.edu/download/datasets/irmas) and change the paths to the training and testing splits at the settings file: `./experiments/settings.txt` 11 | 12 | ## Preprocessing 13 | 14 | Before to run the preprocessing script, please, create specific folder for every model which you would like to preprocess the data for. 15 | The following notation is using (consult `./experiments/settings.txt`): `$IRMAS_HOME_DIRECTORY/IRMAS-TrainingData-Features/model_name` and `$IRMAS_HOME_DIRECTORY/IRMAS-TestingData-Features/model_name`, where `model_name` is referenced to one of the model filenames in `./experiments/models` folder. 16 | Usage: 17 | 18 | ```bash 19 | python preprocessing.py -m model_name 20 | ``` 21 | 22 | Currently supported models are `han16`, `singlelayer`, `multilayer` which are referenced in the paper as han16, 23 | single-layer and multi-layer models respectively. 24 | 25 | ## Training 26 | 27 | Usage: 28 | 29 | ```bash 30 | python training.py -m model_name -o optimizer_name [-l] 31 | ``` 32 | 33 | The option `-l` states for loading data into RAM at the beginning of the experiment instead of reading it batch-by-batch from the disk. 34 | 35 | ## Evaluation 36 | 37 | Usage: 38 | 39 | ```bash 40 | python evaluation.py -m model_name -w /path/to/weights/file.hdf5 -s evaluation_strategy 41 | ``` 42 | 43 | The weights for the reported models can be found at `./weights/model_name` folder. 44 | 45 | Evaluation strategies are: 46 | The `s1` strategy computes a mean activation through whole audio excerpt and apply identification threshold 47 | The `s2` strategy computes sum of activations, normalize it by dividing by maximum activation. 48 | 49 | ## Reference 50 | 51 | References are coming. -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/__init__.py -------------------------------------------------------------------------------- /evaluation.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import importlib 3 | import os 4 | 5 | import numpy as np 6 | import pandas as pd 7 | from keras import backend as K 8 | from sklearn.metrics import f1_score, precision_score, recall_score 9 | from sklearn.preprocessing import MultiLabelBinarizer 10 | 11 | from experiments.settings import * 12 | 13 | 14 | class Evaluator(object): 15 | 16 | def __init__(self, model_module, weights_path, evaluation_strategy="s2"): 17 | """ 18 | Test metadata format 19 | --------------------- 20 | filename : string 21 | class_ids: string of ints with space as a delimiter 22 | """ 23 | test_dataset = pd.read_csv(IRMAS_TESTING_META_PATH, names=["filename", "class_ids"]) 24 | self.X = list(test_dataset.filename) 25 | targets = [[int(category) for category in target.split()] for target in test_dataset.class_ids] 26 | self.ml_binarizer = MultiLabelBinarizer().fit(targets) 27 | self.y_true = self.ml_binarizer.transform(targets) 28 | 29 | self.y_pred = np.zeros(shape=self.y_true.shape) 30 | self.y_pred_raw = np.zeros(shape=self.y_true.shape) 31 | self.y_pred_raw_average = np.zeros(shape=self.y_true.shape) 32 | self.model_module = model_module 33 | self.weights_path = weights_path 34 | self.feature_filenames = os.listdir(os.path.join(IRMAS_TEST_FEATURE_BASEPATH, model_module.BASE_NAME)) 35 | self.dataset_mean = np.load(os.path.join(MODEL_MEANS_BASEPATH, "{}_mean.npy".format(model_module.BASE_NAME))) 36 | self.evaluation_strategy = evaluation_strategy 37 | self.thresholds_s1 = [0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24] 38 | self.thresholds_s2 = [0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60] 39 | 40 | def compute_prediction_scores_raw(self, model): 41 | for i, (data_for_excerpt, filename) in enumerate(self._batch_generator(self.X)): 42 | one_excerpt_prediction = model.predict_on_batch(data_for_excerpt) 43 | if self.evaluation_strategy == "s2": 44 | self.y_pred_raw[i, :] = self._compute_prediction_sum(one_excerpt_prediction) 45 | else: 46 | self.y_pred_raw_average[i, :] = self._compute_prediction_sum(one_excerpt_prediction) 47 | 48 | def report_metrics(self, threshold): 49 | for average_strategy in ["micro", "macro"]: 50 | print("{} average strategy, threshold {}".format(average_strategy, threshold)) 51 | print("precision:\t{}".format(precision_score(self.y_true, self.y_pred, average=average_strategy))) 52 | print("recall:\t{}".format(recall_score(self.y_true, self.y_pred, average=average_strategy))) 53 | print("f1:\t{}".format(f1_score(self.y_true, self.y_pred, average=average_strategy))) 54 | 55 | def evaluate(self): 56 | model = self.model_module.build_model(IRMAS_N_CLASSES) 57 | model.load_weights(self.weights_path) 58 | model.compile(optimizer="sgd", loss="categorical_crossentropy") 59 | self.compute_prediction_scores_raw(model) 60 | if self.evaluation_strategy == "s2": 61 | for threshold in self.thresholds_s2: 62 | self.y_pred = np.copy(self.y_pred_raw) 63 | for i in range(self.y_pred.shape[0]): 64 | self.y_pred[i, :] /= self.y_pred[i, :].max() 65 | self.y_pred[self.y_pred >= threshold] = 1 66 | self.y_pred[self.y_pred < threshold] = 0 67 | self.report_metrics(threshold) 68 | else: 69 | for threshold in self.thresholds_s1: 70 | self.y_pred = np.copy(self.y_pred_raw_average) 71 | self.y_pred[self.y_pred < threshold] = 0 72 | self.y_pred[self.y_pred > threshold] = 1 73 | self.report_metrics(threshold) 74 | 75 | def _load_features(self, audio_filename): 76 | features = list() 77 | for feature_filename in self.feature_filenames: 78 | if audio_filename in feature_filename: 79 | filename_full_path = os.path.join(IRMAS_TEST_FEATURE_BASEPATH, 80 | self.model_module.BASE_NAME, 81 | feature_filename) 82 | feature = np.load(filename_full_path) 83 | feature -= self.dataset_mean 84 | features.append(feature) 85 | 86 | if K.image_dim_ordering() == "th": 87 | features = np.array(features).reshape(-1, 1, self.model_module.N_MEL_BANDS, self.model_module.SEGMENT_DUR) 88 | else: 89 | features = np.array(features).reshape(-1, self.model_module.N_MEL_BANDS, self.model_module.SEGMENT_DUR, 1) 90 | return features 91 | 92 | def _batch_generator(self, inputs): 93 | for audio_filename in inputs: 94 | yield self._load_features(audio_filename), audio_filename 95 | 96 | def _compute_prediction_sum(self, predictions): 97 | prediction_sum = np.zeros(IRMAS_N_CLASSES) 98 | for prediction in predictions: 99 | prediction_sum += prediction 100 | if self.evaluation_strategy == "s1": # simple averaging strategy 101 | prediction_sum /= predictions.shape[0] 102 | return prediction_sum 103 | 104 | 105 | def main(): 106 | aparser = argparse.ArgumentParser() 107 | aparser.add_argument("-m", 108 | action="store", 109 | dest="model", 110 | help="-m model to evaluate") 111 | aparser.add_argument("-w", 112 | action="store", 113 | dest="weights_path", 114 | help="-w path to file with weights for selected model") 115 | aparser.add_argument("-s", 116 | action="store", 117 | dest="evaluation_strategy", 118 | default="s2", 119 | help="-s evaluation strategy: `s1` (simple averaging and thresholding) or `s2` (" 120 | "summarization, normalization by max probability and thresholding)") 121 | 122 | args = aparser.parse_args() 123 | 124 | if not (args.model and args.weights_path): 125 | aparser.error("Please, specify the model and the weights path to evaluate!") 126 | try: 127 | if args.model in ALLOWED_MODELS: 128 | model_module = importlib.import_module(".{}".format(args.model), "experiments.models") 129 | print "{} imported as 'model'".format(args.model) 130 | else: 131 | print "The specified model is not allowed" 132 | if not os.path.exists(args.weights_path): 133 | print "The specified weights path doesn't exist" 134 | except ImportError, e: 135 | print e 136 | 137 | evaluator = Evaluator(model_module, args.weights_path, args.evaluation_strategy) 138 | evaluator.evaluate() 139 | 140 | 141 | if __name__ == "__main__": 142 | main() 143 | -------------------------------------------------------------------------------- /experiments/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/experiments/__init__.py -------------------------------------------------------------------------------- /experiments/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/experiments/models/__init__.py -------------------------------------------------------------------------------- /experiments/models/han16.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | 4 | from keras import backend as K 5 | from keras.models import Sequential 6 | from keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, Dense 7 | from keras.layers.advanced_activations import LeakyReLU 8 | 9 | import librosa 10 | 11 | from ..settings import * 12 | 13 | MODEL_KEY = 'han_base_model' 14 | BASE_NAME = os.path.splitext(os.path.basename(__file__))[0] 15 | N_SEGMENTS_PER_TRAINING_FILE = 3 16 | SAMPLES_PER_EPOCH = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * TRAIN_SPLIT 17 | SAMPLES_PER_VALIDATION = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * VALIDATION_SPLIT 18 | N_MEL_BANDS = 128 19 | SEGMENT_DUR = 43 20 | 21 | 22 | def build_model(n_classes): 23 | model = Sequential() 24 | if K.image_dim_ordering() == 'th': 25 | input_shape = (1, N_MEL_BANDS, SEGMENT_DUR) 26 | else: 27 | input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1) 28 | 29 | model.add(ZeroPadding2D(padding=(1, 1), input_shape=input_shape)) 30 | model.add(Convolution2D(32, 3, 3, 31 | border_mode='same', 32 | init='glorot_uniform', 33 | name='block1_conv1')) 34 | model.add(LeakyReLU(alpha=0.33)) 35 | model.add(ZeroPadding2D(padding=(1, 1))) 36 | model.add(Convolution2D(32, 3, 3, 37 | border_mode='same', 38 | init='glorot_uniform', 39 | name='block1_conv2')) 40 | model.add(LeakyReLU(alpha=0.33)) 41 | model.add(MaxPooling2D((3, 3), strides=(3, 3), name='block1_pool')) 42 | model.add(Dropout(0.25)) 43 | model.add(ZeroPadding2D(padding=(1, 1))) 44 | model.add(Convolution2D(64, 3, 3, 45 | border_mode='same', 46 | init='glorot_uniform', 47 | name='block2_conv1')) 48 | model.add(LeakyReLU(alpha=0.33)) 49 | model.add(ZeroPadding2D(padding=(1, 1))) 50 | model.add(Convolution2D(64, 3, 3, 51 | border_mode='same', 52 | init='glorot_uniform', 53 | name='block2_conv2')) 54 | model.add(LeakyReLU(alpha=0.33)) 55 | model.add(MaxPooling2D((3, 3), strides=(3, 3), name='block2_pool')) 56 | model.add(Dropout(0.25)) 57 | model.add(ZeroPadding2D(padding=(1, 1))) 58 | model.add(Convolution2D(128, 3, 3, 59 | border_mode='same', 60 | init='glorot_uniform', 61 | name='block3_conv1')) 62 | model.add(ZeroPadding2D(padding=(1, 1))) 63 | model.add(LeakyReLU(alpha=0.33)) 64 | model.add(Convolution2D(128, 3, 3, 65 | border_mode='same', 66 | init='glorot_uniform', 67 | name='block3_conv2')) 68 | model.add(LeakyReLU(alpha=0.33)) 69 | model.add(MaxPooling2D((3, 3), strides=(3, 3), name='block3_pool')) 70 | model.add(Dropout(0.25)) 71 | model.add(ZeroPadding2D(padding=(1, 1))) 72 | model.add(Convolution2D(256, 3, 3, 73 | border_mode='same', 74 | init='glorot_uniform', 75 | name='block4_conv1')) 76 | model.add(ZeroPadding2D(padding=(1, 1))) 77 | model.add(LeakyReLU(alpha=0.33)) 78 | model.add(Convolution2D(256, 3, 3, 79 | border_mode='same', 80 | init='glorot_uniform', 81 | name='block4_conv2')) 82 | model.add(LeakyReLU(alpha=0.33)) 83 | model.add(GlobalMaxPooling2D()) 84 | model.add(Dense(1024, 85 | name='fc1')) 86 | model.add(LeakyReLU(alpha=0.33)) 87 | model.add(Dropout(0.5)) 88 | model.add(Dense(n_classes, activation='sigmoid', name='prediction')) 89 | return model 90 | 91 | 92 | def compute_spectrograms(filename): 93 | out_rate = 22050 94 | 95 | frames, rate = librosa.load(filename, sr=out_rate, mono=True) 96 | if len(frames) < out_rate: 97 | # if less then 1 second - can't process 98 | raise Exception("Audio duration is too short") 99 | 100 | normalized_audio = _normalize(frames) 101 | melspectr = librosa.feature.melspectrogram(y=normalized_audio, sr=out_rate, n_mels=N_MEL_BANDS, fmax=out_rate/2) 102 | logmelspectr = librosa.logamplitude(melspectr**2, ref_power=1.0) 103 | 104 | # now going through spectrogram with the stride of the segment duration 105 | for start_idx in range(0, logmelspectr.shape[1] - SEGMENT_DUR + 1, SEGMENT_DUR): 106 | yield logmelspectr[:, start_idx:start_idx + SEGMENT_DUR] 107 | 108 | 109 | def _normalize(audio_signal): 110 | return audio_signal/np.amax(audio_signal) -------------------------------------------------------------------------------- /experiments/models/multilayer.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from keras import backend as K 4 | from keras.models import Model 5 | from keras.layers import Convolution2D, BatchNormalization, MaxPooling2D, Flatten 6 | from keras.layers import Input, Dense, Dropout, merge 7 | from keras.layers.advanced_activations import ELU 8 | from keras.regularizers import l2 9 | 10 | from ..settings import * 11 | 12 | MODEL_KEY = 'multi_layer_musically_motivated_model' 13 | BASE_NAME = os.path.splitext(os.path.basename(__file__))[0] 14 | N_SEGMENTS_PER_TRAINING_FILE = 1 15 | SAMPLES_PER_EPOCH = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * TRAIN_SPLIT 16 | SAMPLES_PER_VALIDATION = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * VALIDATION_SPLIT 17 | N_MEL_BANDS = 96 18 | SEGMENT_DUR = 128 19 | 20 | 21 | def build_model(n_classes): 22 | 23 | if K.image_dim_ordering() == 'th': 24 | input_shape = (1, N_MEL_BANDS, SEGMENT_DUR) 25 | channel_axis = 1 26 | else: 27 | input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1) 28 | channel_axis = 3 29 | melgram_input = Input(shape=input_shape) 30 | 31 | maxpool_const = 4 32 | m_sizes = [5, 80] 33 | n_sizes = [1, 3, 5] 34 | n_filters = [128, 64, 32] 35 | 36 | layers = list() 37 | 38 | for m_i in m_sizes: 39 | for i, n_i in enumerate(n_sizes): 40 | x = Convolution2D(n_filters[i], m_i, n_i, 41 | border_mode='same', 42 | init='he_normal', 43 | W_regularizer=l2(1e-5), 44 | name=str(n_i)+'_'+str(m_i)+'_'+'conv')(melgram_input) 45 | x = BatchNormalization(axis=channel_axis, mode=0, name=str(n_i)+'_'+str(m_i)+'_'+'bn')(x) 46 | x = ELU()(x) 47 | x = MaxPooling2D(pool_size=(N_MEL_BANDS/maxpool_const, SEGMENT_DUR/maxpool_const), 48 | name=str(n_i)+'_'+str(m_i)+'_'+'pool')(x) 49 | layers.append(x) 50 | 51 | x = merge(layers, mode='concat', concat_axis=channel_axis) 52 | 53 | x = Dropout(0.25)(x) 54 | x = Convolution2D(128, 3, 3, init='he_normal', W_regularizer=l2(1e-5), border_mode='same', name='conv2')(x) 55 | x = BatchNormalization(axis=channel_axis, mode=0, name='bn2')(x) 56 | x = ELU()(x) 57 | x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool2')(x) 58 | 59 | x = Dropout(0.25)(x) 60 | x = Convolution2D(128, 3, 3, init='he_normal', W_regularizer=l2(1e-5), border_mode='same', name='conv3')(x) 61 | x = BatchNormalization(axis=channel_axis, mode=0, name='bn3')(x) 62 | x = ELU()(x) 63 | x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name='pool3')(x) 64 | 65 | x = Flatten(name='flatten')(x) 66 | x = Dropout(0.5)(x) 67 | x = Dense(256, init='he_normal', W_regularizer=l2(1e-5), name='fc1')(x) 68 | x = ELU()(x) 69 | x = Dropout(0.5)(x) 70 | x = Dense(n_classes, init='he_normal', W_regularizer=l2(1e-5), activation='softmax', name='prediction')(x) 71 | model = Model(melgram_input, x) 72 | 73 | return model 74 | -------------------------------------------------------------------------------- /experiments/models/singlelayer.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from keras import backend as K 4 | from keras.models import Model 5 | from keras.layers import Convolution2D, BatchNormalization, MaxPooling2D, Flatten, Dense 6 | from keras.layers import Input, Dropout, merge 7 | from keras.layers.advanced_activations import ELU 8 | from keras.regularizers import l2 9 | 10 | import librosa 11 | 12 | from ..settings import * 13 | 14 | MODEL_KEY = 'single_layer_musically_motivated_model' 15 | BASE_NAME = os.path.splitext(os.path.basename(__file__))[0] 16 | N_SEGMENTS_PER_TRAINING_FILE = 1 17 | SAMPLES_PER_EPOCH = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * TRAIN_SPLIT 18 | SAMPLES_PER_VALIDATION = N_TRAINING_SET * N_SEGMENTS_PER_TRAINING_FILE * VALIDATION_SPLIT 19 | N_MEL_BANDS = 96 20 | SEGMENT_DUR = 128 21 | 22 | 23 | def build_model(n_classes): 24 | 25 | if K.image_dim_ordering() == 'th': 26 | input_shape = (1, N_MEL_BANDS, SEGMENT_DUR) 27 | channel_axis = 1 28 | else: 29 | input_shape = (N_MEL_BANDS, SEGMENT_DUR, 1) 30 | channel_axis = 3 31 | melgram_input = Input(shape=input_shape) 32 | 33 | m_sizes = [50, 70] 34 | n_sizes = [1, 3, 5] 35 | n_filters = [128, 64, 32] 36 | maxpool_const = 4 37 | 38 | layers = list() 39 | 40 | for m_i in m_sizes: 41 | for i, n_i in enumerate(n_sizes): 42 | x = Convolution2D(n_filters[i], m_i, n_i, 43 | border_mode='same', 44 | init='he_normal', 45 | W_regularizer=l2(1e-5), 46 | name=str(n_i)+'_'+str(m_i)+'_'+'conv')(melgram_input) 47 | x = BatchNormalization(axis=channel_axis, mode=0, name=str(n_i)+'_'+str(m_i)+'_'+'bn')(x) 48 | x = ELU()(x) 49 | x = MaxPooling2D(pool_size=(N_MEL_BANDS, SEGMENT_DUR/maxpool_const), name=str(n_i)+'_'+str(m_i)+'_'+'pool')(x) 50 | x = Flatten(name=str(n_i)+'_'+str(m_i)+'_'+'flatten')(x) 51 | layers.append(x) 52 | 53 | x = merge(layers, mode='concat', concat_axis=channel_axis) 54 | x = Dropout(0.5)(x) 55 | x = Dense(n_classes, init='he_normal', W_regularizer=l2(1e-5), activation='softmax', name='prediction')(x) 56 | model = Model(melgram_input, x) 57 | 58 | return model 59 | 60 | 61 | def compute_spectrograms(filename): 62 | out_rate = 12000 63 | N_FFT = 512 64 | HOP_LEN = 256 65 | 66 | frames, rate = librosa.load(filename, sr=out_rate, mono=True) 67 | if len(frames) < out_rate*3: 68 | # if less then 3 second - can't process 69 | raise Exception("Audio duration is too short") 70 | 71 | logam = librosa.power_to_db 72 | melgram = librosa.feature.melspectrogram 73 | x = logam(melgram(y=frames, sr=out_rate, hop_length=HOP_LEN, 74 | n_fft=N_FFT, n_mels=N_MEL_BANDS) ** 2, 75 | ref=1.0) 76 | 77 | # now going through spectrogram with the stride of the segment duration 78 | for start_idx in range(0, x.shape[1] - SEGMENT_DUR + 1, SEGMENT_DUR): 79 | yield x[:, start_idx:start_idx + SEGMENT_DUR] 80 | -------------------------------------------------------------------------------- /experiments/settings.py: -------------------------------------------------------------------------------- 1 | IRMAS_TRAIN_DATA_PATH = '~/IRMAS/IRMAS-TrainingData/' 2 | IRMAS_TEST_DATA_PATH = '~/IRMAS/IRMAS-TestingData/' 3 | IRMAS_TRAIN_FEATURE_BASEPATH = '~/IRMAS/IRMAS-TrainingData-Features' 4 | IRMAS_TEST_FEATURE_BASEPATH = '~/IRMAS/IRMAS-TestingData-Features' 5 | IRMAS_TRAINING_META_PATH = './metadata/irmas_train_meta.csv' 6 | IRMAS_TESTING_META_PATH = './metadata/irmas_test_meta.csv' 7 | MODEL_WEIGHT_BASEPATH = './weights/' 8 | MODEL_HISTORY_BASEPATH = './history/' 9 | MODEL_MEANS_BASEPATH = './means/' 10 | IRMAS_N_CLASSES = 11 11 | TRAIN_SPLIT = 0.85 12 | VALIDATION_SPLIT = 0.15 13 | N_TRAINING_SET = 6705 14 | MAX_EPOCH_NUM = 400 15 | EARLY_STOPPING_EPOCH = 20 16 | SGD_LR_REDUCE = 5 17 | BATCH_SIZE = 16 18 | ALLOWED_MODELS = ['han16', 'singlelayer', 'multilayer'] 19 | -------------------------------------------------------------------------------- /means/han16_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/means/han16_mean.npy -------------------------------------------------------------------------------- /means/multilayer_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/means/multilayer_mean.npy -------------------------------------------------------------------------------- /means/singlelayer_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/means/singlelayer_mean.npy -------------------------------------------------------------------------------- /metadata/classes.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | S'cel' 3 | p1 4 | aS'pia' 5 | p2 6 | aS'vio' 7 | p3 8 | aS'cla' 9 | p4 10 | aS'org' 11 | p5 12 | aS'gac' 13 | p6 14 | aS'tru' 15 | p7 16 | aS'flu' 17 | p8 18 | aS'sax' 19 | p9 20 | aS'gel' 21 | p10 22 | aS'voi' 23 | p11 24 | a. -------------------------------------------------------------------------------- /metadata/irmas_test_meta.csv: -------------------------------------------------------------------------------- 1 | "[1] - 03 - Alexandre Lagoya - Canarios (Sanz)-4.wav",5 2 | "01. 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Another World-19.wav",4 1 2801 | "(02) dont kill the whale-14.wav",9 10 2802 | "04 Zeca Baleiro - Com Jards Macale-1.wav",7 5 2803 | "Metallica - Nothing Else Matters-21.wav",5 9 10 2804 | "04 - kokoku-5.wav",4 10 2805 | "01 - Led Zeppelin - Custard Pie-3.wav",9 10 2806 | "Jelly Roll Morton - Honky Tonk Blues-10.wav",1 10 2807 | "15_more than a feeling - boston-5.wav",10 2808 | "01 Julian Bream - Albeniz- Mallorca, Opus 202-4.wav",5 2809 | "agw_-_03_-_st. elmo's fire-5.wav",5 1 10 2810 | "01 Blue Train-28.wav",1 2811 | "07 palestrina - stabat mater-13.wav",10 2812 | "nine inch nails - the fragile cd1 - 12 - the great below-8.wav",1 2813 | "13 Chet Atkins & Jerry Reed - Sneakin' Around-17.wav",5 9 2814 | "Reuben Wilson - Inner City Blues (Makes Me Wanna Holler)-2.wav",9 4 2815 | "08 - Rufus Wainwright - Poses-16.wav",1 10 2816 | "08-Concerto in A major, K 219 -2- Adagio-14.wav",3 2 2817 | "07. Buddy DeFranco & Tommy Gumina - Scrapple from the Apple-6.wav",3 2818 | "01 - Wee Baby Blues-3.wav",9 1 10 2819 | "05 - Sonata in A minor, Op. post. 143 D.784 - I. Allegro giusto-11.wav",1 2820 | "17 - Sur le fil-8.wav",1 2821 | "101-eric_clapton-i_feel_free-9.wav",1 10 2822 | "01 - Honky Cat-25.wav",1 2823 | "Mireille Mathieu & Ennio Morricone - 01 - Un Jour Tu Reviendras-5.wav",5 10 2824 | "The Beatles - 16 - Eleanor Rigby-5.wav",10 2825 | "Iron Maiden - 11 - The Evil That Men Do-5.wav",9 10 2826 | "06 - Lali Puna - Faking the Books-25.wav",5 2827 | "02 Stardust-10.wav",1 6 2828 | "01 the other side of the crash_over-3.wav",9 10 2829 | "Gesualdo - Io tacero-7.wav",10 2830 | "Queen of the Stone Age - 08 - Go With the Flow-10.wav",9 1 10 2831 | "[1] - 03 - Alexandre Lagoya - Canarios (Sanz)-5.wav",5 2832 | "01. In The Crease-21.wav",1 8 2833 | "105-new_order-blue_monday-uf-16.wav",9 10 2834 | "102 - killing joke - love like blood-8.wav",9 4 2835 | "01. The Chosen Hour-2.wav",10 2836 | "Reuben Wilson - Inner City Blues (Makes Me Wanna Holler)-11.wav",9 4 2837 | "01. It Don't Mean a Thing (If It Ain't Got That Swing)-5.wav",1 2838 | "210_depeche_mode-world_in_my_eyes-srm-11.wav",10 2839 | "gyorgy ligeti- atmospheres-2.wav",3 7 6 2840 | "01. Trouble (No. 2)-10.wav",9 4 8 2841 | "gyorgy ligeti- atmospheres-7.wav",3 7 6 2842 | "207-Dave Douglas-The Jones-12.wav",4 8 2843 | "1998 - HammerFall - Legacy of kings - Heeding the call-17.wav",10 2844 | "01 Emily-7.wav",0 2 10 2845 | "03-the_beatles-lucy_in_the_sky_with_diamonds-13.wav",9 10 2846 | "04 Red Bats With Teeth-6.wav",1 8 2847 | "brian eno - apollo- atmospheres & soundtracks - 01 - under stars-10.wav",4 2848 | "01. Another World-13.wav",4 1 10 2849 | "06 Night Train-2.wav",9 4 2850 | "Yngwie Malmsteen - Gimme gimme gimme-15.wav",9 10 2851 | "Alice in Chains - Head Creeps-20.wav",9 2852 | "06 - the human league - reproduction - empire state human-7.wav",10 2853 | "14-god is an astronaut - all is violent, all is bright-12.wav",9 4 2854 | "tomahawk-rape_this_day-11.wav",9 2855 | "07 - Store Bought Bones - 07-3.wav",9 4 10 2856 | "Pantera - Floods-23.wav",10 2857 | "Zamfir - The Lonely Shepherd - 01 - The Lonely Shepherd-7.wav",7 2858 | "01 - Canto das tre╠Вs rac╠зas-6.wav",5 10 2859 | "06 - New Noise-2.wav",9 2860 | "02-the_mars_volta-the_widow-jrp-14.wav",9 4 10 2861 | "02 entangled-11.wav",5 9 10 2862 | "arvo p_rt - alina - 01 - spiegel im spiegel-17.wav",1 2 2863 | "1968 - saucerfull - 03 - set the controls for the heart of the sun-7.wav",4 10 2864 | "01. Flight Time-23.wav",7 1 2865 | "06 - The partisan-4.wav",5 10 2866 | "Philip Glass - Cello Octet Conjunto Ib_rico - Glass Reflections - 03 - Mishima-9.wav",0 2867 | "08 - Rufus Wainwright - Poses-23.wav",1 2868 | "john_coltrane-01-a_love_supreme_part_1-rns-9.wav",1 8 2869 | "01 Chuck Mangione_Feels So Good_Feels So Good-1.wav",5 6 2870 | "Fingerstyle Guitar Magazine #47 - 01 - Liona Boyd - Carretera Libertad-2.wav",5 2871 | "radiohead - 01 - airbag-18.wav",9 10 2872 | "03_fly like an eagle - steve miller-4.wav",9 4 10 2873 | "pi soundtrack - 06 - massive attack - angel-6.wav",10 2874 | "talk talk - spirit of eden - 05 - i believe in you-7.wav",9 2875 | -------------------------------------------------------------------------------- /preprocessing.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import importlib 3 | import os 4 | import numpy as np 5 | 6 | from experiments.settings import * 7 | 8 | 9 | def preprocess(model_module): 10 | for data_path, feature_path in [(IRMAS_TRAIN_DATA_PATH, IRMAS_TRAIN_FEATURE_BASEPATH), 11 | (IRMAS_TEST_DATA_PATH, IRMAS_TEST_FEATURE_BASEPATH)]: 12 | for root, dirs, files in os.walk(data_path): 13 | files = [filename for filename in files if filename.endswith('.wav')] 14 | for filename in files: 15 | for i, spec_segment in enumerate(model_module.compute_spectrograms(os.path.join(root, filename))): 16 | feature_filename = os.path.join(feature_path, model_module.BASE_NAME, 17 | "{filename}_{segment_idx}".format(filename=filename, 18 | segment_idx=i)) 19 | np.save(feature_filename, spec_segment) 20 | 21 | 22 | def main(): 23 | aparser = argparse.ArgumentParser() 24 | aparser.add_argument('-m', 25 | action='store', 26 | dest='model', 27 | help='-m model for preprocessing') 28 | args = aparser.parse_args() 29 | 30 | if not args.model: 31 | aparser.error('Please, specify the model!') 32 | try: 33 | if args.model in ALLOWED_MODELS: 34 | model_module = importlib.import_module(".{}".format(args.model), "experiments.models") 35 | print "{} imported as 'model'".format(args.model) 36 | else: 37 | print "The specified model is not allowed" 38 | except ImportError, e: 39 | print e 40 | preprocess(model_module) 41 | 42 | 43 | if __name__ == "__main__": 44 | main() 45 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | keras 2 | librosa 3 | numpy 4 | pandas 5 | sklearn -------------------------------------------------------------------------------- /training.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import importlib 3 | import os 4 | import pickle 5 | 6 | import numpy as np 7 | import pandas as pd 8 | from keras import backend as K 9 | from keras.utils.np_utils import to_categorical 10 | from keras.optimizers import SGD 11 | from keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler 12 | from keras.metrics import fbeta_score 13 | from sklearn.model_selection import train_test_split 14 | 15 | from experiments.settings import * 16 | 17 | 18 | class Trainer(object): 19 | init_lr = 0.001 20 | 21 | def __init__(self, X_train, X_val, y_train, y_val, model_module, optimizer, load_to_memory): 22 | self.model_module = model_module 23 | self.dataset_mean = np.load(os.path.join(MODEL_MEANS_BASEPATH, "{}_mean.npy".format(model_module.BASE_NAME))) 24 | self.optimizer = optimizer if optimizer != 'sgd' else SGD(lr=self.init_lr, momentum=0.9, nesterov=True) 25 | self.in_memory_data = load_to_memory 26 | extended_x_train, extended_y_train = self._get_extended_data(X_train, y_train) 27 | extended_x_val, extended_y_val = self._get_extended_data(X_val, y_val) 28 | self.y_train = extended_y_train 29 | self.y_val = extended_y_val 30 | if self.in_memory_data: 31 | self.X_train = self._load_features(extended_x_train) 32 | self.X_val = self._load_features(extended_x_val) 33 | else: 34 | self.X_train = extended_x_train 35 | self.X_val = extended_x_val 36 | 37 | def _load_features(self, filenames): 38 | features = list() 39 | for filename in filenames: 40 | feature_filename = os.path.join(IRMAS_TRAIN_FEATURE_BASEPATH, self.model_module.BASE_NAME, 41 | "{}.npy".format(filename)) 42 | feature = np.load(feature_filename) 43 | feature -= self.dataset_mean 44 | features.append(feature) 45 | 46 | if K.image_dim_ordering() == 'th': 47 | features = np.array(features).reshape(-1, 1, self.model_module.N_MEL_BANDS, self.model_module.SEGMENT_DUR) 48 | else: 49 | features = np.array(features).reshape(-1, self.model_module.N_MEL_BANDS, self.model_module.SEGMENT_DUR, 1) 50 | return features 51 | 52 | def _get_extended_data(self, inputs, targets): 53 | extended_inputs = list() 54 | for i in range(0, self.model_module.N_SEGMENTS_PER_TRAINING_FILE): 55 | extended_inputs.extend(['_'.join(list(x)) for x in zip(inputs, [str(i)]*len(inputs))]) 56 | extended_inputs = np.array(extended_inputs) 57 | extended_targets = np.tile(np.array(targets).reshape(-1), 58 | self.model_module.N_SEGMENTS_PER_TRAINING_FILE).reshape(-1, IRMAS_N_CLASSES) 59 | return extended_inputs, extended_targets 60 | 61 | def _batch_generator(self, inputs, targets): 62 | assert len(inputs) == len(targets) 63 | while True: 64 | indices = np.arange(len(inputs)) 65 | np.random.shuffle(indices) 66 | for start_idx in range(0, len(inputs) - BATCH_SIZE + 1, BATCH_SIZE): 67 | excerpt = indices[start_idx:start_idx + BATCH_SIZE] 68 | if self.in_memory_data: 69 | yield inputs[excerpt], targets[excerpt] 70 | else: 71 | yield self._load_features(inputs[excerpt]), targets[excerpt] 72 | 73 | def train(self): 74 | model = self.model_module.build_model(IRMAS_N_CLASSES) 75 | 76 | early_stopping = EarlyStopping(monitor='val_loss', patience=EARLY_STOPPING_EPOCH) 77 | save_clb = ModelCheckpoint( 78 | "{weights_basepath}/{model_path}/".format( 79 | weights_basepath=MODEL_WEIGHT_BASEPATH, 80 | model_path=self.model_module.BASE_NAME) + 81 | "epoch.{epoch:02d}-val_loss.{val_loss:.3f}-fbeta.{val_fbeta_score:.3f}"+"-{key}.hdf5".format( 82 | key=self.model_module.MODEL_KEY), 83 | monitor='val_loss', 84 | save_best_only=True) 85 | lrs = LearningRateScheduler(lambda epoch_n: self.init_lr / (2**(epoch_n//SGD_LR_REDUCE))) 86 | model.summary() 87 | model.compile(optimizer=self.optimizer, 88 | loss='categorical_crossentropy', 89 | metrics=['accuracy', fbeta_score]) 90 | 91 | history = model.fit_generator(self._batch_generator(self.X_train, self.y_train), 92 | samples_per_epoch=self.model_module.SAMPLES_PER_EPOCH, 93 | nb_epoch=MAX_EPOCH_NUM, 94 | verbose=2, 95 | callbacks=[save_clb, early_stopping, lrs], 96 | validation_data=self._batch_generator(self.X_val, self.y_val), 97 | nb_val_samples=self.model_module.SAMPLES_PER_VALIDATION, 98 | class_weight=None, 99 | nb_worker=1) 100 | 101 | pickle.dump(history.history, open('{history_basepath}/{model_path}/history_{model_key}.pkl'.format( 102 | history_basepath=MODEL_HISTORY_BASEPATH, 103 | model_path=self.model_module.BASE_NAME, 104 | model_key=self.model_module.MODEL_KEY), 105 | 'w')) 106 | 107 | 108 | def main(): 109 | dataset = pd.read_csv(IRMAS_TRAINING_META_PATH, names=["filename", "class_id"]) 110 | X_train, X_val, y_train, y_val = train_test_split(list(dataset.filename), 111 | to_categorical(np.array(dataset.class_id, dtype=int)), 112 | test_size=VALIDATION_SPLIT) 113 | aparser = argparse.ArgumentParser() 114 | aparser.add_argument('-m', 115 | action='store', 116 | dest='model', 117 | help='-m model to train') 118 | aparser.add_argument('-o', 119 | action='store', 120 | dest='optimizer', 121 | default='sgd', 122 | help='-o optimizer') 123 | aparser.add_argument('-l', 124 | action='store_true', 125 | dest='load_to_memory', 126 | default=False, 127 | help='-l load dataset to memory') 128 | args = aparser.parse_args() 129 | 130 | if not args.model: 131 | aparser.error('Please, specify the model to train!') 132 | try: 133 | if args.model in ALLOWED_MODELS: 134 | model_module = importlib.import_module(".{}".format(args.model), "experiments.models") 135 | print "{} imported as 'model'".format(args.model) 136 | else: 137 | print "The specified model is not allowed" 138 | except ImportError, e: 139 | print e 140 | 141 | trainer = Trainer(X_train, X_val, y_train, y_val, model_module, args.optimizer, args.load_to_memory) 142 | trainer.train() 143 | 144 | 145 | if __name__ == "__main__": 146 | main() 147 | -------------------------------------------------------------------------------- /weights/multilayer/epoch.128-val_loss.0.866-fbeta.0.782.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/weights/multilayer/epoch.128-val_loss.0.866-fbeta.0.782.hdf5 -------------------------------------------------------------------------------- /weights/singlelayer/epoch.115-val_loss.0.902-fbeta.0.673.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Veleslavia/EUSIPCO2017/fde71fa637d48d589cd3569522236db14b7cb46e/weights/singlelayer/epoch.115-val_loss.0.902-fbeta.0.673.hdf5 --------------------------------------------------------------------------------