Speech Coding, Speech Interfaces and IOT - Opportunities and Challenges

Bäckström, Tom

doi:10.1109/acssc.2018.8645502

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…This endeavour is usually referred to as "embedded audio". Notable examples, conceived for the makers community, are Axoloti 20 and Prynth [175]. According to the results reported in [176], the state-of-the-art in this space is represented by Bela [177].…”

Section: Relevant Work Common To Both Iomust and Ioautmentioning

confidence: 99%

The Internet of Sounds: Convergent Trends, Insights, and Future Directions

Turchet

Lagrange

Rottondi

et al. 2023

IEEE Internet Things J.

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Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of Sound and Music Computing with that of the Internet of Things. This paper proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and nonmusical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate soundbased services and applications that are globally available to the users. We survey the state of the art in this space, discuss the technological and non-technological challenges ahead of us and propose a comprehensive research agenda for the field.

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Section: Relevant Work Common To Both Iomust and Ioautmentioning

confidence: 99%

The Internet of Sounds: Convergent Trends, Insights, and Future Directions

Turchet

Lagrange

Rottondi

et al. 2023

IEEE Internet Things J.

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“…The estimated shape parameter of the noise in the GG model, β N , is determined by using ( 6) and (7). The optimum threshold value, T B , for removing noise from noisy speech at the coefficient-level, through the soft threshold function in (2), is determined using (5).…”

Section: ) Parameter Setup Of the Ggfc Algorithmmentioning

confidence: 99%

Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

2020

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This work aims at developing an adaptive wavelet thresholding algorithm for speech enhancement with significant performance improvement over other wavelet-based counterparts. This is accomplished through the formulation of the optimum threshold for noise reduction, based on the generalized Gaussian priors to fully characterize the statistics of speech and noise wavelet coefficients. In addition, through the frame-wise context modeling which enables tracking of the statistical characteristics of each individual coefficient on the frame-wise basis, the optimum threshold is accurate and adaptive at both the coefficient level and frame level. The frame-wise context model is formulated by virtue of the context subspace projection of the wavelet coefficients, with the context index employed as the invariant correspondence between successive frame parameters, thereby enabling the frame-wise tracking at the coefficient level. Simulation results show significant improvement over the wavelet-based speech enhancement algorithms in terms of the segmental signal-to-noise ratio improvement by as much as 226%, the perceptual evaluation of speech quality by 36%, the short-time objective intelligibility by 17.8% and the cepstral distance by 33.3%. When benchmarked with the well-established short-time-Fouriertransform-based counterparts, the proposed wavelet thresholding algorithm offers favorable and more robust performances, particularly under non-stationary noise conditions, with no adverse musical noise effect.INDEX TERMS context modeling, speech enhancement, wavelet thresholding.

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“…Wireless acoustic sensor networks (WASNs) can be used to sample the spatial acoustic space to gain a better signal quality than a single sensor ever could [1], [2]. Moreover, ad-hoc WASNs, viz., collections of all arbitrary available devices with microphones, can work as WASNs with very low hardware costs.…”

Section: Introductionmentioning

confidence: 99%

PyAWNeS-Codec: Speech and audio codec for ad-hoc acoustic wireless sensor networks

Bäckström

Bouafif

Zarazaga

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Existing hardware with microphones can potentially be used as sensor networks to capture speech and audio signals for the benefit of better signal quality than possible with a single microphone. A central pre-requisite for such ad-hoc acoustic wireless sensor networks (ASWNs) is an efficient communication protocol with which to transmit audio data between nodes. For that purpose, we present the world's-first speech and audio codec especially designed for ASWNs, which has competitive quality also in single-channel operation. To ensure quality in the single-channel scenario, it closely resembles conventional codecs of the TCX-type, but extended with features to facilitate multi-device operation, including dithered quantization, delay estimation and compensation, as well as multi-channel postfiltering. The codec is intended to become a baseline for future research and we therefore provide it as an open-access library. Our experiments confirm that performance is in the same range as recent commercial single-channel codecs and that added devices improve quality.Index Terms-speech and audio coding, ad-hoc acoustic sensor networks, time difference of arrival estimation, delay compensation, multi-channel post-filtering

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Speech Coding, Speech Interfaces and IOT - Opportunities and Challenges

Cited by 7 publications

References 20 publications

The Internet of Sounds: Convergent Trends, Insights, and Future Directions

The Internet of Sounds: Convergent Trends, Insights, and Future Directions

Performance Improvement of Adaptive Wavelet Thresholding for Speech Enhancement Using Generalized Gaussian Priors and Frame-Wise Context Modeling

PyAWNeS-Codec: Speech and audio codec for ad-hoc acoustic wireless sensor networks

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