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Journal articles on the topic 'Multichannel audio'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Ono, Kazuho. "2.Multichannel Audio." Journal of the Institute of Image Information and Television Engineers 68, no. 8 (2014): 604–7. http://dx.doi.org/10.3169/itej.68.604.

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2

Holbrook, Kyle A., and Michael J. Yacavone. "Multichannel audio reproduction system." Journal of the Acoustical Society of America 82, no. 2 (1987): 728. http://dx.doi.org/10.1121/1.395373.

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3

Emmett, John. "Metering for Multichannel Audio." SMPTE Journal 110, no. 8 (2001): 532–36. http://dx.doi.org/10.5594/j17765.

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4

Zhu, Qiushi, Jie Zhang, Yu Gu, Yuchen Hu, and Lirong Dai. "Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19768–76. http://dx.doi.org/10.1609/aaai.v38i17.29951.

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Self-supervised speech pre-training methods have developed rapidly in recent years, which show to be very effective for many near-field single-channel speech tasks. However, far-field multichannel speech processing is suffering from the scarcity of labeled multichannel data and complex ambient noises. The efficacy of self-supervised learning for far-field multichannel and multi-modal speech processing has not been well explored. Considering that visual information helps to improve speech recognition performance in noisy scenes, in this work we propose the multichannel multi-modal speech self-s

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5

Gao, Xue Fei, Guo Yang, Jing Wang, Xiang Xie, and Jing Ming Kuang. "A Backward Compatible Multichannel Audio Compression Method." Advanced Materials Research 756-759 (September 2013): 977–81. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.977.

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This paper proposes a backward-compatible multichannel audio codec based on downmix and upmix operation. The codec represents a multichannel audio input signal with downmixed mono signal and spatial parametric data. The encoding method consists of three parts: spatial temporal analysis of audio signal, compressing multi-channel audio into mono audio and encoding mono signals. The proposed codec combines high audio quality and low parameter coding rate and the method is simpler and more effective than the conventional methods. With this method, its possible to transmit or store multi-channel au

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6

Martyniuk, Tetiana, Maksym Mykytiuk, and Mykola Zaitsev. "FEATURES OF ANALYSIS OF MULTICHANNEL AUDIO SIGNALSFEATURES OF ANALYSIS OF MULTICHANNEL AUDIO SIGNALS." ГРААЛЬ НАУКИ, no. 2-3 (April 9, 2021): 302–5. http://dx.doi.org/10.36074/grail-of-science.02.04.2021.061.

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The rapid growth of audio content has led to the need to use tools for analysis and quality control of audio signals using software and hardware and modules. The fastest-growing industry is software and programming languages.The Python programming language today has the most operational and visual capabilities for working with sound. When developing programs for computational signal analysis, it provides the optimal balance of high and low-level programming functions. Compared to Matlab or other similar solutions, Python is free and allows you to create standalone applications without the need

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7

Gunawan, Teddy Surya, and Mira Kartiwi. "Performance Evaluation of Multichannel Audio Compression." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 146. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp146-153.

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<p>In recent years, multichannel audio systems are widely used in modern sound devices as it can provide more realistic and engaging experience to the listener. This paper focuses on the performance evaluation of three lossy, i.e. AAC, Ogg Vorbis, and Opus, and three lossless compression, i.e. FLAC, TrueAudio, and WavPack, for multichannel audio signals, including stereo, 5.1 and 7.1 channels. Experiments were conducted on the same three audio files but with different channel configurations. The performance of each encoder was evaluated based on its encoding time (averaged over 100 times

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Teddy, Surya Gunawan, and Kartiwi Mira. "Performance Evaluation of Multichannel Audio Compression." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 146–53. https://doi.org/10.11591/ijeecs.v10.i1.pp146-153.

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In recent years, multichannel audio systems are widely used in modern sound devices as it can provide more realistic and engaging experience to the listener. This paper focuses on the performance evaluation of three lossy, i.e. AAC, Ogg Vorbis, and Opus, and three lossless compression, i.e. FLAC, TrueAudio, and WavPack, for multichannel audio signals, including stereo, 5.1 and 7.1 channels. Experiments were conducted on the same three audio files but with different channel configurations. The performance of each encoder was evaluated based on its encoding time (averaged over 100 times), data r

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9

Fujimori, Kazuki, Bisser Raytchev, Kazufumi Kaneda, et al. "Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks." Journal of Robotics and Mechatronics 33, no. 3 (2021): 515–25. http://dx.doi.org/10.20965/jrm.2021.p0515.

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We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.

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10

Dong, Yingjun, Neil G. MacLaren, Yiding Cao, et al. "Utterance Clustering Using Stereo Audio Channels." Computational Intelligence and Neuroscience 2021 (September 25, 2021): 1–8. http://dx.doi.org/10.1155/2021/6151651.

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Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was

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11

Hotho, Gerard, Lars F. Villemoes, and Jeroen Breebaart. "A Backward-Compatible Multichannel Audio Codec." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 1 (2008): 83–93. http://dx.doi.org/10.1109/tasl.2007.910768.

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12

Fujimori, Kazuki, Bisser Raytchev, Kazufumi Kaneda, et al. "Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks." Journal of Robotics and Mechatronics 33, no. 3 (2021): 515–25. https://doi.org/10.5281/zenodo.14819486.

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(Uploaded by Plazi for the Bat Literature Project) We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.

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13

Chen, Ling, Wei Wang, and Cheng Jiang. "Research on Embedded Multichannel Audio Conversion Module." Journal of Physics: Conference Series 2625, no. 1 (2023): 012075. http://dx.doi.org/10.1088/1742-6596/2625/1/012075.

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Abstract With the rapid development and progress of information technology and the wide usage of audio signal processing in underwater acoustic signal processing, acoustic audio signal acquisition, conversion, and transmission technology always play an important role. To enhance the ability of signal acquisition and conversion with high reliability, this paper designs an embedded multi-channel audio conversion module. The module achieves multi-channel, multi-sample rates with synchronous analog-to-digital/digital-to-analog conversion (ADC/DAC) function, and additionally equips with dual networ

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14

Yao, Shu-Nung, and Chang-Wei Huang. "Autonomous Technology for 2.1 Channel Audio Systems." Electronics 11, no. 3 (2022): 339. http://dx.doi.org/10.3390/electronics11030339.

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During the COVID-19 pandemic, smart home requirements have shifted toward entertainment at home. The purpose of this research project was therefore to develop a robotic audio system for home automation. High-end audio systems normally refer to multichannel home theaters. Although multichannel audio systems enable people to enjoy surround sound as they do at the cinema, stereo audio systems have been popularly used since the 1980s. The major shortcoming of a stereo audio system is its narrow listening area. If listeners are out of the area, the system has difficulty providing a stable sound fie

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15

Zhu, Yunxi, Wenyao Ma, Zheng Kuang, Ming Wu, and Jun Yang. "Optimal audio beam pattern synthesis for an enhanced parametric array loudspeaker." Journal of the Acoustical Society of America 154, no. 5 (2023): 3210–22. http://dx.doi.org/10.1121/10.0022415.

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A parametric array loudspeaker (PAL) generates highly directional audible sound in air with a small aperture size compared to a conventional loudspeaker. But in indoor applications, the long propagation distance of a PAL causes reflections, which disturbs the reproduction of narrow audio beams. Moreover, sound distortion appears along the off-axis direction due to the frequency dependence of the beam width. This study proposed an optimal audio beam pattern synthesis for a PAL-based convex optimization, which can design the audio beam of a PAL with an optimal solution. The proposed method overc

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16

Hidri Adel, Meddeb Souad, Abdulqadir Alaqeeli, and Amiri Hamid. "Beamforming Techniques for Multichannel audio Signal Separation." International Journal of Digital Content Technology and its Applications 6, no. 20 (2012): 659–67. http://dx.doi.org/10.4156/jdcta.vol6.issue20.72.

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17

Wrigley, S. N., G. J. Brown, V. Wan, and S. Renals. "Speech and crosstalk detection in multichannel audio." IEEE Transactions on Speech and Audio Processing 13, no. 1 (2005): 84–91. http://dx.doi.org/10.1109/tsa.2004.838531.

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18

Gulsrud, Timothy. "Acoustical design of multichannel audio listening environments." Journal of the Acoustical Society of America 123, no. 5 (2008): 3202. http://dx.doi.org/10.1121/1.2933360.

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19

Sarensen, J. A. "High-Fidelity Multichannel Audio Coding [Book Review." IEEE Signal Processing Magazine 22, no. 5 (2005): 150–53. http://dx.doi.org/10.1109/msp.2005.1511837.

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20

Sobirin, Muhammad, and Ikhwana Elfitri. "Perancangan dan Analisis Kinerja Pengkodean Audio Multichannel Dengan Metode Closed Loop." Jurnal Nasional Teknik Elektro 3, no. 2 (2014): 157–66. http://dx.doi.org/10.20449/jnte.v3i2.80.

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21

Lee, Dongheon, and Jung-Woo Choi. "Inter-channel Conv-TasNet for source-agnostic multichannel audio enhancement." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (2023): 2068–75. http://dx.doi.org/10.3397/in_2022_0297.

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Deep neural network (DNN) models for the audio enhancement task have been developed in various ways. Most of them rely on the source-dependent characteristics, such as temporal or spectral characteristics of speeches, to suppress noises embedded in measured signals. Only a few studies have attempted to exploit the spatial information embedded in multichannel data. In this work, we propose a DNN architecture that fully exploits inter-channel relations to realize source-agnostic audio enhancement. The proposed model is based on the fully convolutional time-domain audio separation network (Conv-T

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22

Mattos, Tiago F., and Bennett M. Brooks. "Comparison of recording studio control room operational response measurements for single, stereo, and immersive audio monitor configurations." Journal of the Acoustical Society of America 152, no. 4 (2022): A104. http://dx.doi.org/10.1121/10.0015691.

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Audio monitoring in recording studio control rooms has evolved continuously since the beginning of multitrack systems. In recent years, control rooms have adapted to using multiple audio monitors (loudspeakers) needed for the immersive audio experience. The primary technical recommendations for determining the acoustical quality of a control room are given in EBU-TECH-3276 and ITU BS.1116. The results of measuring the Operational Room Response Curve (ORRC) can differ significantly for only one audio monitor operating compared to the two monitors required for stereo. For multichannel immersive

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23

Smith, William P. "Method of decoding two-channel matrix encoded audio to reconstruct multichannel audio." Journal of the Acoustical Society of America 120, no. 2 (2006): 573. http://dx.doi.org/10.1121/1.2336656.

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24

Li, Zhen, and Qian Yi Yang. "The Research of Dynamic Sound of Multichannel System Based on Matlab." Applied Mechanics and Materials 602-605 (August 2014): 2569–71. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2569.

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With the emergence and development of 3D video technology, audiences have higher expectations to the sound effect while watching the 3D video. So we studied the dynamic sound effect under the existing audio standard of the multichannel. In this paper, the audio file of the sea wave was disposed by the Gaussian function through MATLAB and divided into several parts to be saved as different audio files. Then each audio file was sent to a channel to simulate a stereo sound field. Test result showed that the effect will provide a perfect experience for the audiences.

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25

Pulkki, V., and T. Hirvonen. "Localization of virtual sources in multichannel audio reproduction." IEEE Transactions on Speech and Audio Processing 13, no. 1 (2005): 105–19. http://dx.doi.org/10.1109/tsa.2004.838533.

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26

Faller, C. "Parametric multichannel audio coding: synthesis of coherence cues." IEEE Transactions on Audio, Speech and Language Processing 14, no. 1 (2006): 299–310. http://dx.doi.org/10.1109/tsa.2005.854105.

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27

Nugraha, Aditya Arie, Antoine Liutkus, and Emmanuel Vincent. "Multichannel Audio Source Separation With Deep Neural Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 9 (2016): 1652–64. http://dx.doi.org/10.1109/taslp.2016.2580946.

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28

Leglaive, Simon, Roland Badeau, and Gael Richard. "Multichannel Audio Source Separation With Probabilistic Reverberation Priors." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 12 (2016): 2453–65. http://dx.doi.org/10.1109/taslp.2016.2614140.

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29

Elfitri, I., Banu Günel, and A. M. Kondoz. "Multichannel Audio Coding Based on Analysis by Synthesis." Proceedings of the IEEE 99, no. 4 (2011): 657–70. http://dx.doi.org/10.1109/jproc.2010.2102310.

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30

Pulkki, V., and M. Karjalainen. "Multichannel audio rendering using amplitude panning [DSP Applications]." IEEE Signal Processing Magazine 25, no. 3 (2008): 118–22. http://dx.doi.org/10.1109/msp.2008.918025.

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31

Miron, Marius, Julio J. Carabias-Orti, Juan J. Bosch, Emilia Gómez, and Jordi Janer. "Score-Informed Source Separation for Multichannel Orchestral Recordings." Journal of Electrical and Computer Engineering 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/8363507.

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This paper proposes a system for score-informed audio source separation for multichannel orchestral recordings. The orchestral music repertoire relies on the existence of scores. Thus, a reliable separation requires a good alignment of the score with the audio of the performance. To that extent, automatic score alignment methods are reliable when allowing a tolerance window around the actual onset and offset. Moreover, several factors increase the difficulty of our task: a high reverberant image, large ensembles having rich polyphony, and a large variety of instruments recorded within a distan

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32

Dewi Nurdiyah, Eko Mulyanto Yuniarno, Yoyon Kusnendar Suprapto, and Mauridhi Hery Purnomo. "IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio." EMITTER International Journal of Engineering Technology 11, no. 2 (2023): 246–64. http://dx.doi.org/10.24003/emitter.v11i2.827.

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A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indones

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33

Qiao, Yue, Léo Guadagnin, and Edgar Choueiri. "Isolation performance metrics for personal sound zone reproduction systems." JASA Express Letters 2, no. 10 (2022): 104801. http://dx.doi.org/10.1121/10.0014604.

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Two isolation performance metrics, inter-zone isolation (IZI) and inter-program isolation (IPI), are introduced for evaluating personal sound zone (PSZ) systems. Compared to the commonly used acoustic contrast metric, IZI and IPI are generalized for multichannel audio and quantify the isolation of sound zones and of audio programs, respectively. The two metrics are shown to be generally non-interchangeable and suitable for different scenarios, such as generating dark zones (IZI) or minimizing audio-on-audio interference (IPI). Furthermore, two examples with free-field simulations are presented

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34

Noll, Peter, and Davis Pan. "ISO/MPEG Audio Coding." International Journal of High Speed Electronics and Systems 08, no. 01 (1997): 69–118. http://dx.doi.org/10.1142/s0129156497000044.

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The Moving Pictures Expert Group within the International Organization of Standardization (ISO/MPEG) has developed, and is presently developing, a series of audiovisual standards. Its audio coding standard MPEG Phase 1 is the first international standard in the field of high quality digital audio compression and has been applied in many areas, both for consumer and professional audio. Typical application areas for digital audio are in the fields of audio production, program distribution and exchange, digital sound broadcasting, digital storage, and various multimedia applications. This paper w

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35

Lee, Tae-Jin, Jae-Hyoun Yoo, Jeong-Il Seo, Kyeong-Ok Kang, and Whan-Woo Kim. "Multichannel Audio Reproduction Technology based on 10.2ch for UHDTV." Journal of Broadcast Engineering 17, no. 5 (2012): 827–37. http://dx.doi.org/10.5909/jbe.2012.17.5.827.

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36

Dai Yang, Hongmei Ai, C. Kyriakakis, and C. C. J. Kuo. "High-fidelity multichannel audio coding with karhunen-loeve transform." IEEE Transactions on Speech and Audio Processing 11, no. 4 (2003): 365–80. http://dx.doi.org/10.1109/tsa.2003.814375.

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37

Olson, Bruce C. "Using 3‐D modeling to design multichannel audio systems." Journal of the Acoustical Society of America 113, no. 4 (2003): 2201. http://dx.doi.org/10.1121/1.4780191.

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38

Ye, Qinghua, Hefei Yang, and Xiaodong Li. "A simplified crosstalk cancellation method for multichannel audio equalization." Journal of the Acoustical Society of America 131, no. 4 (2012): 3218. http://dx.doi.org/10.1121/1.4708000.

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39

Bharitkar, Sunil, Grant Davidson, Louis Fielder, and Poppy Crum. "Tutorial on Critical Listening of Multichannel Audio Codec Performance." SMPTE Motion Imaging Journal 121, no. 8 (2012): 30–45. http://dx.doi.org/10.5594/j18246xy.

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40

Bayram, Ilker. "A Multichannel Audio Denoising Formulation Based on Spectral Sparsity." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 12 (2015): 2272–85. http://dx.doi.org/10.1109/taslp.2015.2479042.

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41

Pagès, Guilhem, Roberto Longo, Laurent Simon, and Manuel Melon. "Online adaptive identification of multichannel systems for audio applications." Journal of the Acoustical Society of America 155, no. 1 (2024): 229–40. http://dx.doi.org/10.1121/10.0024149.

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Impulse responses (IRs) estimation of multi-input acoustic systems is a prerequisite for many audio applications. In this paper, an adaptive identification problem based on the Autostep algorithm is extended to the simultaneous estimation of room IRs for multiple input single output linear time invariant systems without any a priori information. To do so, the proposed algorithm is initially evaluated in a simulated room with several sound sources active at the same time. Finally, an experimental validation is proposed for the cases of a semi-anechoic chamber and an arbitrary room. Special atte

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42

Neubauer, Bernd. "Neue Ära der Audioübertragung." Bühnentechnische Rundschau 119, Sonderband-2025 (2025): 86–88. https://doi.org/10.5771/0007-3091-2025-sonderband-2025-086.

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Sennheiser bringt mit Spectera das erste bidirektionale, drahtlose Breitband-Ecosystem auf den Markt, das dank WMAS-Technologie (Wireless Multichannel Audio Systems) die Komplexität drahtloser Systeme erheblich reduziert. Es kommt mit deutlich weniger Hardware aus, vereinfacht die Frequenzkoordination, ist redundant ausgelegt und bietet viel Flexibilität. Von Bernd Neubauer

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43

Houge, Benjamin, and Jutta Friedrichs. "Food Opera: A New Genre for Audio-gustatory Expression." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 5 (2021): 59–63. http://dx.doi.org/10.1609/aiide.v9i5.12652.

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“Food opera” is the term that the authors have applied to a new genre of audio-gustatory experience, in which a multi-course meal is paired with real-time, algorithmically generated music, deployed over a massively multichannel sound system. This paper presents an overview of the system used to deploy the sonic component of these events, while also exploring the history and creative potential of this unique multisensory format.

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Lee, Yong Ju, Jeongil Seo, Seungkwon Beack, et al. "Design and Development of T-DMB Multichannel Audio Service System Based on Spatial Audio Coding." ETRI Journal 31, no. 4 (2009): 365–75. http://dx.doi.org/10.4218/etrij.09.0108.0557.

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45

KRON, Thorben, Thomas KOCH, Christoph ENDE, et al. "AES67 based microphone array for sound source localization." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 5 (2024): 6015–24. http://dx.doi.org/10.3397/in_2024_3674.

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Within the research project EAV-Infra, a novel microphone array system was developed. The system stands out for its utilization of Ethernet-based signal, control data and power transmission, ensuring a high degree of flexibility in operation. The key feature is its ability to facilitate real-time multichannel audio streaming via AES67, allowing for scalable and distributed recording and computing. With a total of 64 digital miniaturized microphones integrated into its printed circuit board-based design, the system allows for analysis of audio data for beamforming and other sound localization m

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46

Wu, Jiayue. "Empowering Musicians: Innovating Virtual Ensemble Concert Music with Networked Audio Technology." Virtual Worlds 4, no. 1 (2025): 9. https://doi.org/10.3390/virtualworlds4010009.

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This study investigates the application of network audio technology in performing arts and media art collaborations within virtual environments, analyzing its impact through four case studies. Employing a practice-based research methodology through using a variety of open-source software and communication protocols, it examines the cultural and social dynamics, creative workflows, and technical frameworks of ensembles leveraging network audio technology for remote recording and virtual production. These projects, recognized internationally within the electroacoustic music community, underscore

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47

Sánchez-Hevia, Héctor A., Roberto Gil-Pita, and Manuel Rosa-Zurera. "Efficient multichannel detection of impulsive audio events for wireless networks." Applied Acoustics 179 (August 2021): 108005. http://dx.doi.org/10.1016/j.apacoust.2021.108005.

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48

Vernony, Steve, and Tony Spath. "Carrying Multichannel Audio in a Stereo Production and Distribution Infrastructure." SMPTE Journal 111, no. 2 (2002): 97–102. http://dx.doi.org/10.5594/j16393.

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49

Hong, Jin‐Woo, Dae‐Young Jang, and Seong‐Han Kim. "Multichannel audio signal compression and quality assessment for AV communications." Journal of the Acoustical Society of America 103, no. 5 (1998): 3027. http://dx.doi.org/10.1121/1.422552.

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50

Kendall, Gary S. "Spatial Perception and Cognition in Multichannel Audio for Electroacoustic Music." Organised Sound 15, no. 03 (2010): 228–38. http://dx.doi.org/10.1017/s1355771810000336.

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