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This paper presents three cascade algorithms for combined acoustic feedback cancelation (AFC) and noise reduction (NR) in speech applications. A prediction error method (PEM)-based adaptive feedback cancelatio...
A three-stage approach is proposed for speaker counting and speech separation in noisy and reverberant environments. In the spatial feature extraction, a spatial coherence matrix (SCM) is computed using whiten...
In this paper, we propose a technique for removing a specific type of interference from a monaural recording. Nonstationary interferences are generally challenging to eliminate from such recordings. However, i...
Traffic congestion can lead to negative driving emotions, significantly increasing the likelihood of traffic accidents. Reducing negative driving emotions as a means to mitigate speeding, reckless overtaking, ...
Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions. F...
In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) a...
In the past decades, convolutional neural networks (CNNs) have been commonly adopted in audio perception tasks, which aim to learn latent representations. However, for audio analysis, CNNs may exhibit limitati...
The presence of noise and reverberation significantly impedes speech clarity and intelligibility. To mitigate these effects, numerous deep learning-based network models have been proposed for speech enhancemen...
In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computation...
Personalized voice triggering is a key technology in voice assistants and serves as the first step for users to activate the voice assistant. Personalized voice triggering involves keyword spotting (KWS) and s...
The goal of sound event detection and localization (SELD) is to identify each individual sound event class and its activity time from a piece of audio, while estimating its spatial location at the time of acti...
Analysis of couple interactions using speech processing techniques is an increasingly active multi-disciplinary field that poses challenges such as automatic relationship quality assessment and behavioral codi...
More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can b...
In recent years, the adoption of deep learning techniques has allowed to obtain major breakthroughs in the automatic music generation research field, sparking a renewed interest in generative music. A great de...
Emotion plays a dominant role in speech. The same utterance with different emotions can lead to a completely different meaning. The ability to perform various of emotion during speaking is also one of the typi...
Speech emotion recognition (SER) is a hot topic in speech signal processing. With the advanced development of the cheap computing power and proliferation of research in data-driven methods, deep learning appro...
Recent years have witnessed a great progress in single-channel speech separation by applying self-attention based networks. Despite the excellent performance in mining relevant long-sequence contextual informa...
The human auditory system employs a number of principles to facilitate the selection of perceptually separated streams from a complex sound mixture. The brain leverages multi-scale redundant representations of...
Music inpainting is a sub-task of automated music generation that aims to infill incomplete musical pieces to help musicians in their musical composition process. Many methods have been developed for this task...
A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a single-channel single-frame post-...
In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, s...
Voice activity detection remains a significant challenge in the presence of transients since transients are more dominant than speech, though it has achieved satisfactory performance in quasi-stationary noisy ...
With the rise of deep learning, spoken language understanding (SLU) for command-and-control applications such as a voice-controlled virtual assistant can offer reliable hands-free operation to physically disab...
Spoken language recognition has made significant progress in recent years, for which automatic speech recognition has been used as a parallel branch to extract phonetic features. However, there is still a lack...
We present a new dataset of 3000 artificial music tracks with rich annotations based on real instrument samples and generated by algorithmic composition with respect to music theory. Our collection provides gr...
Electromagnetic components greatly contribute to the peculiar timbre of analog audio gear. Indeed, distortion effects due to the nonlinear behavior of magnetic materials are known to play an important role in ...
Most music listeners have an intuitive understanding of the notion of rhythm complexity. Musicologists and scientists, however, have long sought objective ways to measure and model such a distinctively percept...
Speech feature model is the basis of speech and noise separation, speech expression, and different styles of speech conversion. With the development of signal processing methods, the feature types and dimensio...
The original article was published in EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:3
Speech is the most common form of human communication, and many conversations use digital communication links. For efficient transmission, acoustic speech waveforms are usually converted to digital form, with ...
The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit ...
Recently, supervised speech separation has made great progress. However, limited by the nature of supervised training, most existing separation methods require ground-truth sources and are trained on synthetic...
We propose a new task for sound event detection (SED): sound event triage (SET). The goal of SET is to detect an arbitrary number of high-priority event classes while allowing misdetections of low-priority eve...
The aim of this paper is to investigate the influence of personality traits, characterized by the BFI (Big Five Inventory) and its significant revision called BFI-2, on music recommendation error. The BFI-2 de...
SincNet architecture has shown significant benefits over traditional Convolutional Neural Networks (CNN), especially for speaker recognition applications. SincNet comprises parameterized Sinc functions as filt...
The Correction to this article has been published in EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:9
Music source separation (MSS) is to isolate musical instrument signals from the given music mixture. Stripes widely exist in music spectrograms, which potentially indicate high-level music information. For exa...
The purpose of this paper is to show a music mixing system that is capable of automatically mixing separate raw recordings with good quality regardless of the music genre. This work recalls selected methods fo...
For immersive applications, the generation of binaural sound that matches its visual counterpart is crucial to bring meaningful experiences to people in a virtual environment. Recent studies have shown the pos...
Speech emotion recognition (SER) is a hot topic in speech signal processing. When the training data and the test data come from different corpus, their feature distributions are different, which leads to the d...
In recent years, there has been a national craze for metaverse concerts. However, existing meta-universe concert efforts often focus on immersive visual experiences and lack consideration of the musical and au...
Headphones are commonly used in various environments including at home, outside and on public transport. However, the perception and modelling of the interaction of headphone audio and noisy environments is re...
In the task of sound event detection and localization (SEDL) in a complex environment, the acoustic signals of different events usually have nonlinear superposition, so the detection and localization effect is...
Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres, or to create more variety within musical compositions. The sound not only is determined by the choice of the ...
Voice activity detection (VAD) based on deep neural networks (DNN) have demonstrated good performance in adverse acoustic environments. Current DNN-based VAD optimizes a surrogate function, e.g., minimum cross...
Automated audio captioning is a cross-modal translation task that aims to generate natural language descriptions for given audio clips. This task has received increasing attention with the release of freely av...
Large-scale sound recognition data sets typically consist of acoustic recordings obtained from multimedia libraries. As a consequence, modalities other than audio can often be exploited to improve the outputs ...
The original article was published in EURASIP Journal on Audio, Speech, and Music Processing 2013 2013:27
In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. T...
In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and a hidden Markov model (NMF-HMM)....
Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and t...
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2022 Citation Impact
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2.0 - 5-year Impact Factor
1.081 - SNIP (Source Normalized Impact per Paper)
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