""" 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 @staticmethod 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