8,450 research outputs found
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
We propose a novel deep learning model, which supports permutation invariant
training (PIT), for speaker independent multi-talker speech separation,
commonly known as the cocktail-party problem. Different from most of the prior
arts that treat speech separation as a multi-class regression problem and the
deep clustering technique that considers it a segmentation (or clustering)
problem, our model optimizes for the separation regression error, ignoring the
order of mixing sources. This strategy cleverly solves the long-lasting label
permutation problem that has prevented progress on deep learning based
techniques for speech separation. Experiments on the equal-energy mixing setup
of a Danish corpus confirms the effectiveness of PIT. We believe improvements
built upon PIT can eventually solve the cocktail-party problem and enable
real-world adoption of, e.g., automatic meeting transcription and multi-party
human-computer interaction, where overlapping speech is common.Comment: 5 page
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the unsupervised methods in various
real-life acoustic environments. In this light, we review recently developed,
representative deep learning approaches for tackling non-stationary additive
and convolutional degradation of speech with the aim of providing guidelines
for those involved in the development of environmentally robust speech
recognition systems. We separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech recognition systems, as well
as joint front-end and back-end training frameworks
Music Source Separation with Band-split RNN
The performance of music source separation (MSS) models has been greatly
improved in recent years thanks to the development of novel neural network
architectures and training pipelines. However, recent model designs for MSS
were mainly motivated by other audio processing tasks or other research fields,
while the intrinsic characteristics and patterns of the music signals were not
fully discovered. In this paper, we propose band-split RNN (BSRNN), a
frequency-domain model that explictly splits the spectrogram of the mixture
into subbands and perform interleaved band-level and sequence-level modeling.
The choices of the bandwidths of the subbands can be determined by a priori
knowledge or expert knowledge on the characteristics of the target source in
order to optimize the performance on a certain type of target musical
instrument. To better make use of unlabeled data, we also describe a
semi-supervised model finetuning pipeline that can further improve the
performance of the model. Experiment results show that BSRNN trained only on
MUSDB18-HQ dataset significantly outperforms several top-ranking models in
Music Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage
further improves the performance on all four instrument tracks
Probabilistic Modeling Paradigms for Audio Source Separation
This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems
- …