ArticleIEEE/ACM transactions on audio, speech, and language processing2018
Supervised Speech Separation Based on Deep Learning: An Overview.
Article in IEEE/ACM transactions on audio, speech, and language processing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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76 citing papers in PubMed.
- FAV-DenoiseNet: An Audio-Visual Speech Enhancement Framework Based on Conditional Flow Matching and Visual Encoding.Sensors (Basel, Switzerland) · 2026Article
- Model-Informed Speech Enhancement Using Virtual Room Acoustics and Acoustic Descriptor Optimization.Sensors (Basel, Switzerland) · 2026Article
- Acoustic Features and Recognition of Distress Calls inBiology · 2026Article
- C-EMDNet: A Nonlinear Morphological Deep Framework for Robust Speech Enhancement.Sensors (Basel, Switzerland) · 2026Article
- eXCube2: Explainable Brain-Inspired Spiking Neural Network Framework for Emotion Recognition from Audio, Visual and Multimodal Audio-Visual Data.Biomimetics (Basel, Switzerland) · 2026Article
- Real-Time Signal Processing for Distributed Acoustic Sensing and Acoustic Sensing Systems Under Non-Stationary Noise.Sensors (Basel, Switzerland) · 2026Article
- Towards decoupling frontend enhancement and backend recognition in monaural robust ASR.Computer speech & language · 2026Article
- Leveraging spatial cues from cochlear implant microphones to efficiently enhance speech separation in naturalistic listening scenes.Scientific reports · 2025Article
- A Robust Bilinear Framework for Real-Time Speech Separation and Dereverberation in Wearable Augmented Reality.Sensors (Basel, Switzerland) · 2025Article
- Flash Memory for Synaptic Plasticity in Neuromorphic Computing: A Review.Biomimetics (Basel, Switzerland) · 2025Review
- A systematic study of DNN based speech enhancement in reverberant and reverberant-noisy environments.Computer speech & language · 2025Article
- Multichannel speech enhancement for automatic speech recognition: a literature review.PeerJ. Computer science · 2025Article
- Enhancing dysarthric speech recognition through SepFormer and hierarchical attention network models with multistage transfer learning.Scientific reports · 2024Article
- Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction.Sensors (Basel, Switzerland) · 2024Article
- Cluster-Based Pairwise Contrastive Loss for Noise-Robust Speech Recognition.Sensors (Basel, Switzerland) · 2024Article
- Ecology & computer audition: Applications of audio technology to monitor organisms and environment.Heliyon · 2024Review
- 3 directional Inception-ResUNet: Deep spatial feature learning for multichannel singing voice separation with distortion.PloS one · 2024Article
- Deep Harmonic Finesse: Signal Separation in Wearable Systems with Limited Data.Proceedings. Design Automation Conference · 2024Article
- CNN-based noise reduction for multi-channel speech enhancement system with discrete wavelet transform (DWT) preprocessing.PeerJ. Computer science · 2024Article
- Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task.Scientific reports · 2023Article
16 more citing papers are in PubMed but not listed here.
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Abstract
Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. This paper provides a comprehensive overview of the research on deep learning based supervised speech separation in the last several years. We first introduce the background of speech separation and the formulation of supervised separation. Then, we discuss three main components of supervised separation: learning machines, training targets, and acoustic features. Much of the overview is on separation algorithms where we review monaural methods, including speech enhancement (speech-nonspeech separation), speaker separation (multitalker separation), and speech dereverberation, as well as multimicrophone techniques. The important issue of generalization, unique to supervised learning, is discussed. This overview provides a historical perspective on how advances are made. In addition, we discuss a number of conceptual issues, including what constitutes the target source.
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