End-To-End Dilated Variational Autoencoder with Bottleneck Discriminative Loss for Sound Morphing -- A Preliminary Study
We present a preliminary study on an end-to-end variational autoencoder (VAE) for sound morphing. Two VAE variants are compared: VAE with dilation layers (DC-VAE) and VAE only with regular convolutional layers (CC-VAE). We combine the following loss functions: 1) the time-domain mean-squared error for reconstructing the input signal, 2) the Kullback-Leibler divergence to the standard normal distribution in the bottleneck layer, and 3) the classification loss calculated from the bottleneck representation. On a database of spoken digits, we use 1-nearest neighbor classification to show that the sound classes separate in the bottleneck layer. We introduce the Mel-frequency cepstrum coefficient dynamic time warping (MFCC-DTW) deviation as a measure of how well the VAE decoder projects the class center in the latent (bottleneck) layer to the center of the sounds of that class in the audio domain. In terms of MFCC-DTW deviation and 1-NN classification, DC-VAE outperforms CC-VAE. These results for our parametrization and our dataset indicate that DC-VAE is more suitable for sound morphing than CC-VAE, since the DC-VAE decoder better preserves the topology when mapping from the audio domain to the latent space. Examples are given both for morphing spoken digits and drum sounds.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderDynamic Time WarpingGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Variational Information Bottleneck on Vector Quantized Autoencoders
In this paper, we provide an information-theoretic interpretation of the Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss function of the original VQ-VAE can be derived from the variational determ…
Training Invertible Neural Networks as Autoencoders
Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work, we present methods to train Invertible N…
eVAE: Evolutionary Variational Autoencoder
The surrogate loss of variational autoencoders (VAEs) poses various challenges to their training, inducing the imbalance between task fitting and representation inference. To avert this, the existing strategies for VAEs …
DisentanglementImage GenerationText GenerationDeep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses
Variational dimensionality reduction methods are widely used for their accuracy, generative capabilities, and robustness. We introduce a unifying framework that generalizes both such as traditional and state-of-the-art m…
Contrastive LearningDecoderDimensionality ReductionRepresentation LearningAre We Using Autoencoders in a Wrong Way?
Autoencoders are certainly among the most studied and used Deep Learning models: the idea behind them is to train a model in order to reconstruct the same input data. The peculiarity of these models is to compress the in…
Anomaly DetectionClassificationDenoisingDimensionality Reduction