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The last decade brought significant advances in automatic speech recognition (ASR) thanks to the evolution of deep learning methods. ASR systems evolved from pipeline-based systems, that modeled hand-crafted s...
Many end-to-end approaches have been proposed to detect predefined keywords. For scenarios of multi-keywords, there are still two bottlenecks that need to be resolved: (1) the distribution of important data th...
Lately, the self-attention mechanism has marked a new milestone in the field of automatic speech recognition (ASR). Nevertheless, its performance is susceptible to environmental intrusions as the system predic...
Due to the ad hoc nature of wireless acoustic sensor networks, the position of the sensor nodes is typically unknown. This contribution proposes a technique to estimate the position and orientation of the sens...
Estimating time-frequency domain masks for single-channel speech enhancement using deep learning methods has recently become a popular research field with promising results. In this paper, we propose a novel comp...
Multiple sound source localization is a hot issue of concern in recent years. The Single Source Zone (SSZ) based localization methods achieve good performance due to the detection and utilization of the Time-F...
In this paper, we propose a novel feature compensation algorithm based on independent noise estimation, which employs a Gaussian mixture model (GMM) with fewer Gaussian components to rapidly estimate the noise...
When designing closed-loop electro-acoustic systems, which can commonly be found in hearing aids or public address systems, the most challenging task is canceling and/or suppressing the feedback caused by the ...
Recently, the non-intrusive speech quality assessment method has attracted a lot of attention since it does not require the original reference signals. At the same time, neural networks began to be applied to ...
Sound event detection (SED), which is typically treated as a supervised problem, aims at detecting types of sound events and corresponding temporal information. It requires to estimate onset and offset annotat...
Amongst the various characteristics of a speech signal, the expression of emotion is one of the characteristics that exhibits the slowest temporal dynamics. Hence, a performant speech emotion recognition (SER)...
Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce arti...
In this study, we present a deep neural network-based online multi-speaker localization algorithm based on a multi-microphone array. Following the W-disjoint orthogonality principle in the spectral domain, tim...
An amendment to this paper has been published and can be accessed via the original article.
The original article was published in EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:10
Estimating the direction-of-arrival (DOA) of multiple acoustic sources is one of the key technologies for humanoid robots and drones. However, it is a most challenging problem due to a number of factors, inclu...
Localization of multiple speakers using microphone arrays remains a challenging problem, especially in the presence of noise and reverberation. State-of-the-art localization algorithms generally exploit the sp...
There has been little work in the literature on the speaker diarization of meetings with multiple distance microphones since the publications in 2012 related to the last National Institute of Standards (NIST) ...
Nowadays automatic speech recognition (ASR) systems can achieve higher and higher accuracy rates depending on the methodology applied and datasets used. The rate decreases significantly when the ASR system is ...
An integrated version of the minimum variance distortionless response (MVDR) beamformer for speech enhancement using a microphone array has been recently developed, which merges the benefits of imposing constr...
The Correction to this article has been published in EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:15
The presence of degradations in speech signals, which causes acoustic mismatch between training and operating conditions, deteriorates the performance of many speech-based systems. A variety of enhancement tec...
This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal proce...
Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large...
We propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of ...
Two novel methods for speaker separation of multi-microphone recordings that can also detect speakers with infrequent activity are presented. The proposed methods are based on a statistical model of the probab...
We propose a method of dynamically registering out-of-vocabulary (OOV) words by assigning the pronunciations of these words to pre-inserted OOV tokens, editing the pronunciations of the tokens. To do this, we ...
Instrumentalplaying techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval f...
In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estima...
The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solu...
In real applications, environmental effects such as additive noise and room reverberation lead to a mismatch between training and testing signals that substantially reduces the performance of far-field speaker...
In this paper, we investigate the performance of two deep learning paradigms for the audio-based tasks of acoustic scene, environmental sound and domestic activity classification. In particular, a convolutiona...
In this article, we conduct a comprehensive simulation study for the optimal scores of speaker recognition systems that are based on speaker embedding. For that purpose, we first revisit the optimal scores for...
Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect ...
Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions. However, the problem gets complic...
Humanoid robots require to use microphone arrays to acquire speech signals from the human communication partner while suppressing noise, reverberation, and interferences. Unlike many other applications, microp...
Microphone leakage or crosstalk is a common problem in multichannel close-talk audio recordings (e.g., meetings or live music performances), which occurs when a target signal does not only couple into its dedi...
A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, whic...
Estimation problems like room geometry estimation and localization of acoustic reflectors are of great interest and importance in robot and drone audition. Several methods for tackling these problems exist, bu...
Ego-noise, i.e., the noise a robot causes by its own motions, significantly corrupts the microphone signal and severely impairs the robot’s capability to interact seamlessly with its environment. Therefore, su...
A keyword spotting algorithm implemented on an embedded system using a depthwise separable convolutional neural network classifier is reported. The proposed system was derived from a high-complexity system wit...
Ad hoc acoustic networks comprising multiple nodes, each of which consists of several microphones, are addressed. From the ad hoc nature of the node constellation, microphone positions are unknown. Hence, typi...
In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of pro...
In this paper, we introduce a quadratic approach for single-channel noise reduction. The desired signal magnitude is estimated by applying a linear filter to a modified version of the observations’ vector. The...
In order to improve the performance of hand-crafted features to detect playback speech, two discriminative features, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients,...
This paper presents a new approach based on recurrent neural networks (RNN) to the multiclass audio segmentation task whose goal is to classify an audio signal as speech, music, noise or a combination of these...
Binaural sound source localization is an important and widely used perceptually based method and it has been applied to machine learning studies by many researchers based on head-related transfer function (HRT...
Attention-based encoder-decoder models have recently shown competitive performance for automatic speech recognition (ASR) compared to conventional ASR systems. However, how to employ attention models for onlin...
Experimental data combining complementary measures based on the oral airflow signal is presented in this paper, exploring the view that European Portuguese voiced stops are produced in a similar fashion to Ger...
In this paper, we use empirical mode decomposition and Hurst-based mode selection (EMDH) along with deep learning architecture using a convolutional neural network (CNN) to improve the recognition of dysarthri...
We present a novel model adaptation approach to deal with data variability for speaker diarization in a broadcast environment. Expensive human annotated data can be used to mitigate the domain mismatch by mean...
In this paper, we propose a score-informed source separation framework based on non-negative matrix factorization (NMF) and dynamic time warping (DTW) that suits for both offline and online systems. The propos...
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