Latent Vector Recovery of Audio GANs

• URL: http://arxiv.org/abs/2010.08534v1
• Date: Fri, 16 Oct 2020 17:45:35 GMT
• Title: Latent Vector Recovery of Audio GANs
• Abstract summary: We train a deep residual neural network architecture to project audio synthesized by WaveGAN into the corresponding latent space with near identical reconstruction performance. In the case of synthesized audio, the Mean Squared Error (MSE) between the ground truth and recovered latent vector is minimized.
• Score: 6.824692201913679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examine the task of recovering the latent vector of both synthesized and real audio. Previous works recovered latent vectors of given audio through an auto-encoder inspired technique that trains an encoder network either in parallel with the GAN or after the generator is trained. With our approach, we train a deep residual neural network architecture to project audio synthesized by WaveGAN into the corresponding latent space with near identical reconstruction performance. To accommodate for the lack of an original latent vector for real audio, we optimize the residual network on the perceptual loss between the real audio samples and the reconstructed audio of the predicted latent vectors. In the case of synthesized audio, the Mean Squared Error (MSE) between the ground truth and recovered latent vector is minimized as well. We further investigated the audio reconstruction performance when several gradient optimization steps are applied to the predicted latent vector. Through our deep neural network based method of training on real and synthesized audio, we are able to predict a latent vector that corresponds to a reasonable reconstruction of real audio. Even though we evaluated our method on WaveGAN, our proposed method is universal and can be applied to any other GANs.

Related papers

• Geometric Iterative Retrieval for Neural Audio Codec Resynthesis [35.61634772862795]
  We argue that this dichotomy is incomplete and introduce geometric iterative retrieval, a paradigm that uses the RVQ layer hierarchy itself as a natural iterative decomposition in codebook space.<n>We evaluate our method on restoration tasks across speech and music, and show improvements over both single-pass token prediction and one-step regression baselines.
  arXiv  Detail & Related papers  (2026-08-19T17:29:56Z)
• Learning Robust Spatial Representations from Binaural Audio through Feature Distillation [64.36563387033921]
  We investigate the use of a pretraining stage based on feature distillation to learn a robust spatial representation of speech without the need for data labels.<n>Our experiments demonstrate that the pretrained models show improved performance in noisy and reverberant environments.
  arXiv  Detail & Related papers  (2025-08-28T15:43:15Z)
• VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment [101.2489492032816]
  VALL-E R is a robust and efficient zero-shot Text-to-Speech system. This research has the potential to be applied to meaningful projects, including the creation of speech for those affected by aphasia.
  arXiv  Detail & Related papers  (2024-06-12T04:09:44Z)
• Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes [69.03289331433874]
  We present an end-to-end audio rendering approach (Listen2Scene) for virtual reality (VR) and augmented reality (AR) applications. We propose a novel neural-network-based sound propagation method to generate acoustic effects for 3D models of real environments.
  arXiv  Detail & Related papers  (2023-02-02T04:09:23Z)
• An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms [2.3204178451683264]
  In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers based on musical instrument compression. This study investigates the use of stacked convolutional autoencoders for the compression of time-frequency audio representations for a variety of instruments for a single pitch.
  arXiv  Detail & Related papers  (2023-01-18T17:19:04Z)
• High Fidelity Neural Audio Compression [92.4812002532009]
  We introduce a state-of-the-art real-time, high-fidelity, audio leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary.
  arXiv  Detail & Related papers  (2022-10-24T17:52:02Z)
• End-to-End Binaural Speech Synthesis [71.1869877389535]
  We present an end-to-end speech synthesis system that combines a low-bitrate audio system with a powerful decoder. We demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.
  arXiv  Detail & Related papers  (2022-07-08T05:18:36Z)
• DeepA: A Deep Neural Analyzer For Speech And Singing Vocoding [71.73405116189531]
  We propose a neural vocoder that extracts F0 and timbre/aperiodicity encoding from the input speech that emulates those defined in conventional vocoders. As the deep neural analyzer is learnable, it is expected to be more accurate for signal reconstruction and manipulation, and generalizable from speech to singing.
  arXiv  Detail & Related papers  (2021-10-13T01:39:57Z)
• Voice Activity Detection for Transient Noisy Environment Based on Diffusion Nets [13.558688470594674]
  We address voice activity detection in acoustic environments of transients and stationary noises. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying geometric structure. A deep neural network is trained to separate speech from non-speech frames.
  arXiv  Detail & Related papers  (2021-06-25T17:05:26Z)
• End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks [54.43697805589634]
  We propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID.
  arXiv  Detail & Related papers  (2021-04-27T17:12:30Z)
• Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms [30.3491261167433]
  Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. Deep neural networks as efficient feature extractors has enabled the direct use of audio signals for classification purposes. We attempt to recognize musical instruments in polyphonic audio by only feeding their raw waveforms into deep learning models.
  arXiv  Detail & Related papers  (2021-02-13T13:44:46Z)
• Streaming end-to-end multi-talker speech recognition [34.76106500736099]
  We propose the Streaming Unmixing and Recognition Transducer (SURT) for end-to-end multi-talker speech recognition. Our model employs the Recurrent Neural Network Transducer (RNN-T) as the backbone that can meet various latency constraints. Based on experiments on the publicly available LibriSpeechMix dataset, we show that HEAT can achieve better accuracy compared with PIT.
  arXiv  Detail & Related papers  (2020-11-26T06:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.