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  1. Code-switching (CS) refers to the phenomenon of using more than one language in an utterance, and it presents great challenge to automatic speech recognition (ASR) due to the code-switching property in one utt...

    Authors: Yanhua Long, Shuang Wei, Jie Lian and Yijie Li
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:34
  2. Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essent...

    Authors: Nili Cohen, Gershon Hazan, Boaz Schwartz and Sharon Gannot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:33
  3. The minimum mean-square error (MMSE)-based noise PSD estimators have been used widely for speech enhancement. However, the MMSE noise PSD estimators assume that the noise signal changes at a slower rate than t...

    Authors: Sujan Kumar Roy and Kuldip K. Paliwal
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:32
  4. The performance of speech recognition systems trained with neutral utterances degrades significantly when these systems are tested with emotional speech. Since everybody can speak emotionally in the real-world...

    Authors: Masoud Geravanchizadeh, Elnaz Forouhandeh and Meysam Bashirpour
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:31
  5. If music is the language of the universe, musical note onsets may be the syllables for this language. Not only do note onsets define the temporal pattern of a musical piece, but their time-frequency characteri...

    Authors: Mina Mounir, Peter Karsmakers and Toon van Waterschoot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:30
  6. To improve the performance of speech enhancement in a complex noise environment, a joint constrained dictionary learning method for single-channel speech enhancement is proposed, which solves the “cross projec...

    Authors: Linhui Sun, Yunyi Bu, Pingan Li and Zihao Wu
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:29
  7. 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...

    Authors: Alexandru-Lucian Georgescu, Alessandro Pappalardo, Horia Cucu and Michaela Blott
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:28
  8. 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...

    Authors: Gui-Xin Shi, Wei-Qiang Zhang, Guan-Bo Wang, Jing Zhao, Shu-Zhou Chai and Ze-Yu Zhao
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:27
  9. 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...

    Authors: Lujun Li, Yikai Kang, Yuchen Shi, Ludwig Kürzinger, Tobias Watzel and Gerhard Rigoll
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:26
  10. 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...

    Authors: Tobias Gburrek, Joerg Schmalenstroeer and Reinhold Haeb-Umbach
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:25
  11. 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...

    Authors: Ziyi Xu, Samy Elshamy, Ziyue Zhao and Tim Fingscheidt
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:24
  12. 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...

    Authors: Maoshen Jia, Shang Gao and Changchun Bao
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:23
  13. 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...

    Authors: Yong Lü, Han Lin, Pingping Wu and Yitao Chen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:22
  14. 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 ...

    Authors: Marco Gimm, Philipp Bulling and Gerhard Schmidt
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:21
  15. 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 ...

    Authors: Miao Liu, Jing Wang, Weiming Yi and Fang Liu
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:20
  16. 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...

    Authors: Sichen Liu, Feiran Yang, Yin Cao and Jun Yang
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:19
  17. 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)...

    Authors: Duowei Tang, Peter Kuppens, Luc Geurts and Toon van Waterschoot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:18
  18. 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...

    Authors: Yuxuan Ke, Andong Li, Chengshi Zheng, Renhua Peng and Xiaodong Li
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:17
  19. 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...

    Authors: Hodaya Hammer, Shlomo E. Chazan, Jacob Goldberger and Sharon Gannot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:16
  20. An amendment to this paper has been published and can be accessed via the original article.

    Authors: Randall Ali, Toon van Waterschoot and Marc Moonen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:15

    The original article was published in EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:10

  21. 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...

    Authors: Zonglong Bai, Liming Shi, Jesper Rindom Jensen, Jinwei Sun and Mads Græsbøll Christensen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:14
  22. 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...

    Authors: Sushmita Thakallapalli, Suryakanth V. Gangashetty and Nilesh Madhu
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:13
  23. 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) ...

    Authors: Beatriz Martínez-González, José M. Pardo, José A. Vallejo-Pinto, Rubén San-Segundo and Javier Ferreiros
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:12
  24. 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 ...

    Authors: Kacper Radzikowski, Le Wang, Osamu Yoshie and Robert Nowak
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:11
  25. 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...

    Authors: Randall Ali, Toon van Waterschoot and Marc Moonen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:10

    The Correction to this article has been published in EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:15

  26. 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...

    Authors: Yuki Saishu, Amir Hossein Poorjam and Mads Græsbøll Christensen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:9
  27. 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...

    Authors: Chunyan Ji, Thosini Bamunu Mudiyanselage, Yutong Gao and Yi Pan
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:8
  28. 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...

    Authors: Rajat Hebbar, Pavlos Papadopoulos, Ramon Reyes, Alexander F. Danvers, Angelina J. Polsinelli, Suzanne A. Moseley, David A. Sbarra, Matthias R. Mehl and Shrikanth Narayanan
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:7
  29. 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 ...

    Authors: Sören Schulze and Emily J. King
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:6
  30. 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...

    Authors: Bracha Laufer-Goldshtein, Ronen Talmon and Sharon Gannot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:5
  31. 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 ...

    Authors: Norihide Kitaoka, Bohan Chen and Yuya Obashi
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:4
  32. 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...

    Authors: Vincent Lostanlen, Christian El-Hajj, Mathias Rossignol, Grégoire Lafay, Joakim Andén and Mathieu Lagrange
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:3
  33. 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...

    Authors: Yuval Dorfan, Boaz Schwartz and Sharon Gannot
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:2
  34. 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...

    Authors: Jorge Llombart, Dayana Ribas, Antonio Miguel, Luis Vicente, Alfonso Ortega and Eduardo Lleida
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2021 2021:1
  35. 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...

    Authors: Masoud Geravanchizadeh and Sina Ghalamiosgouei
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:20
  36. 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...

    Authors: Shahin Amiriparian, Maurice Gerczuk, Sandra Ottl, Lukas Stappen, Alice Baird, Lukas Koebe and Björn Schuller
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:19
  37. 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...

    Authors: Dong Wang
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:18
  38. 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 ...

    Authors: Cenk Demiroglu, Aslı Beşirli, Yasin Ozkanca and Selime Çelik
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:17
  39. 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...

    Authors: Alif Bin Abdul Qayyum, K. M. Naimul Hassan, Adrita Anika, Md. Farhan Shadiq, Md Mushfiqur Rahman, Md. Tariqul Islam, Sheikh Asif Imran, Shahruk Hossain and Mohammad Ariful Haque
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:16
  40. 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...

    Authors: Gongping Huang, Jingdong Chen, Jacob Benesty, Israel Cohen and Xudong Zhao
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:15
  41. 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...

    Authors: Patrick Meyer, Samy Elshamy and Tim Fingscheidt
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:14
  42. 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...

    Authors: Benjamin Yen and Yusuke Hioka
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:13
  43. 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...

    Authors: Usama Saqib, Sharon Gannot and Jesper Rindom Jensen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:12
  44. 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...

    Authors: Alexander Schmidt, Andreas Brendel, Thomas Haubner and Walter Kellermann
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:11
  45. 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...

    Authors: Peter Mølgaard Sørensen, Bastian Epp and Tobias May
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:10
  46. 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...

    Authors: Loris Nanni, Yandre M. G. Costa, Rafael L. Aguiar, Rafael B. Mangolin, Sheryl Brahnam and Carlos N. Silla Jr.
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:8
  47. 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...

    Authors: Gal Itzhak, Jacob Benesty and Israel Cohen
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:7
  48. 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,...

    Authors: Jichen Yang, Longting Xu, Bo Ren and Yunyun Ji
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:6
  49. 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...

    Authors: Pablo Gimeno, Ignacio Viñals, Alfonso Ortega, Antonio Miguel and Eduardo Lleida
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2020 2020:5

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