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| """ | |
| Implementation of model from: | |
| Kum et al. - "Joint Detection and Classification of Singing Voice Melody Using | |
| Convolutional Recurrent Neural Networks" (2019) | |
| Link: https://www.semanticscholar.org/paper/Joint-Detection-and-Classification-of-Singing-Voice-Kum-Nam/60a2ad4c7db43bace75805054603747fcd062c0d | |
| """ | |
| import torch | |
| from torch import nn | |
| class JDCNet(nn.Module): | |
| """ | |
| Joint Detection and Classification Network model for singing voice melody. | |
| """ | |
| def __init__(self, num_class=722, seq_len=31, leaky_relu_slope=0.01): | |
| super().__init__() | |
| self.num_class = num_class | |
| # input = (b, 1, 31, 513), b = batch size | |
| self.conv_block = nn.Sequential( | |
| nn.Conv2d( | |
| in_channels=1, out_channels=64, kernel_size=3, padding=1, bias=False | |
| ), # out: (b, 64, 31, 513) | |
| nn.BatchNorm2d(num_features=64), | |
| nn.LeakyReLU(leaky_relu_slope, inplace=True), | |
| nn.Conv2d(64, 64, 3, padding=1, bias=False), # (b, 64, 31, 513) | |
| ) | |
| # res blocks | |
| self.res_block1 = ResBlock( | |
| in_channels=64, out_channels=128 | |
| ) # (b, 128, 31, 128) | |
| self.res_block2 = ResBlock( | |
| in_channels=128, out_channels=192 | |
| ) # (b, 192, 31, 32) | |
| self.res_block3 = ResBlock(in_channels=192, out_channels=256) # (b, 256, 31, 8) | |
| # pool block | |
| self.pool_block = nn.Sequential( | |
| nn.BatchNorm2d(num_features=256), | |
| nn.LeakyReLU(leaky_relu_slope, inplace=True), | |
| nn.MaxPool2d(kernel_size=(1, 4)), # (b, 256, 31, 2) | |
| nn.Dropout(p=0.2), | |
| ) | |
| # maxpool layers (for auxiliary network inputs) | |
| # in = (b, 128, 31, 513) from conv_block, out = (b, 128, 31, 2) | |
| self.maxpool1 = nn.MaxPool2d(kernel_size=(1, 40)) | |
| # in = (b, 128, 31, 128) from res_block1, out = (b, 128, 31, 2) | |
| self.maxpool2 = nn.MaxPool2d(kernel_size=(1, 20)) | |
| # in = (b, 128, 31, 32) from res_block2, out = (b, 128, 31, 2) | |
| self.maxpool3 = nn.MaxPool2d(kernel_size=(1, 10)) | |
| # in = (b, 640, 31, 2), out = (b, 256, 31, 2) | |
| self.detector_conv = nn.Sequential( | |
| nn.Conv2d(640, 256, 1, bias=False), | |
| nn.BatchNorm2d(256), | |
| nn.LeakyReLU(leaky_relu_slope, inplace=True), | |
| nn.Dropout(p=0.2), | |
| ) | |
| # input: (b, 31, 512) - resized from (b, 256, 31, 2) | |
| self.bilstm_classifier = nn.LSTM( | |
| input_size=512, hidden_size=256, batch_first=True, bidirectional=True | |
| ) # (b, 31, 512) | |
| # input: (b, 31, 512) - resized from (b, 256, 31, 2) | |
| self.bilstm_detector = nn.LSTM( | |
| input_size=512, hidden_size=256, batch_first=True, bidirectional=True | |
| ) # (b, 31, 512) | |
| # input: (b * 31, 512) | |
| self.classifier = nn.Linear( | |
| in_features=512, out_features=self.num_class | |
| ) # (b * 31, num_class) | |
| # input: (b * 31, 512) | |
| self.detector = nn.Linear( | |
| in_features=512, out_features=2 | |
| ) # (b * 31, 2) - binary classifier | |
| # initialize weights | |
| self.apply(self.init_weights) | |
| def get_feature_GAN(self, x): | |
| seq_len = x.shape[-2] | |
| x = x.float().transpose(-1, -2) | |
| convblock_out = self.conv_block(x) | |
| resblock1_out = self.res_block1(convblock_out) | |
| resblock2_out = self.res_block2(resblock1_out) | |
| resblock3_out = self.res_block3(resblock2_out) | |
| poolblock_out = self.pool_block[0](resblock3_out) | |
| poolblock_out = self.pool_block[1](poolblock_out) | |
| return poolblock_out.transpose(-1, -2) | |
| def get_feature(self, x): | |
| seq_len = x.shape[-2] | |
| x = x.float().transpose(-1, -2) | |
| convblock_out = self.conv_block(x) | |
| resblock1_out = self.res_block1(convblock_out) | |
| resblock2_out = self.res_block2(resblock1_out) | |
| resblock3_out = self.res_block3(resblock2_out) | |
| poolblock_out = self.pool_block[0](resblock3_out) | |
| poolblock_out = self.pool_block[1](poolblock_out) | |
| return self.pool_block[2](poolblock_out) | |
| def forward(self, x): | |
| """ | |
| Returns: | |
| classification_prediction, detection_prediction | |
| sizes: (b, 31, 722), (b, 31, 2) | |
| """ | |
| ############################### | |
| # forward pass for classifier # | |
| ############################### | |
| seq_len = x.shape[-1] | |
| x = x.float().transpose(-1, -2) | |
| convblock_out = self.conv_block(x) | |
| resblock1_out = self.res_block1(convblock_out) | |
| resblock2_out = self.res_block2(resblock1_out) | |
| resblock3_out = self.res_block3(resblock2_out) | |
| poolblock_out = self.pool_block[0](resblock3_out) | |
| poolblock_out = self.pool_block[1](poolblock_out) | |
| GAN_feature = poolblock_out.transpose(-1, -2) | |
| poolblock_out = self.pool_block[2](poolblock_out) | |
| # (b, 256, 31, 2) => (b, 31, 256, 2) => (b, 31, 512) | |
| classifier_out = ( | |
| poolblock_out.permute(0, 2, 1, 3).contiguous().view((-1, seq_len, 512)) | |
| ) | |
| classifier_out, _ = self.bilstm_classifier( | |
| classifier_out | |
| ) # ignore the hidden states | |
| classifier_out = classifier_out.contiguous().view((-1, 512)) # (b * 31, 512) | |
| classifier_out = self.classifier(classifier_out) | |
| classifier_out = classifier_out.view( | |
| (-1, seq_len, self.num_class) | |
| ) # (b, 31, num_class) | |
| # sizes: (b, 31, 722), (b, 31, 2) | |
| # classifier output consists of predicted pitch classes per frame | |
| # detector output consists of: (isvoice, notvoice) estimates per frame | |
| return torch.abs(classifier_out.squeeze()), GAN_feature, poolblock_out | |
| def init_weights(m): | |
| if isinstance(m, nn.Linear): | |
| nn.init.kaiming_uniform_(m.weight) | |
| if m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.Conv2d): | |
| nn.init.xavier_normal_(m.weight) | |
| elif isinstance(m, nn.LSTM) or isinstance(m, nn.LSTMCell): | |
| for p in m.parameters(): | |
| if p.data is None: | |
| continue | |
| if len(p.shape) >= 2: | |
| nn.init.orthogonal_(p.data) | |
| else: | |
| nn.init.normal_(p.data) | |
| class ResBlock(nn.Module): | |
| def __init__(self, in_channels: int, out_channels: int, leaky_relu_slope=0.01): | |
| super().__init__() | |
| self.downsample = in_channels != out_channels | |
| # BN / LReLU / MaxPool layer before the conv layer - see Figure 1b in the paper | |
| self.pre_conv = nn.Sequential( | |
| nn.BatchNorm2d(num_features=in_channels), | |
| nn.LeakyReLU(leaky_relu_slope, inplace=True), | |
| nn.MaxPool2d(kernel_size=(1, 2)), # apply downsampling on the y axis only | |
| ) | |
| # conv layers | |
| self.conv = nn.Sequential( | |
| nn.Conv2d( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=3, | |
| padding=1, | |
| bias=False, | |
| ), | |
| nn.BatchNorm2d(out_channels), | |
| nn.LeakyReLU(leaky_relu_slope, inplace=True), | |
| nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False), | |
| ) | |
| # 1 x 1 convolution layer to match the feature dimensions | |
| self.conv1by1 = None | |
| if self.downsample: | |
| self.conv1by1 = nn.Conv2d(in_channels, out_channels, 1, bias=False) | |
| def forward(self, x): | |
| x = self.pre_conv(x) | |
| if self.downsample: | |
| x = self.conv(x) + self.conv1by1(x) | |
| else: | |
| x = self.conv(x) + x | |
| return x | |