9,945 research outputs found
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data
This paper presents a novel BigEAR big data framework that employs
psychological audio processing chain (PAPC) to process smartphone-based
acoustic big data collected when the user performs social conversations in
naturalistic scenarios. The overarching goal of BigEAR is to identify moods of
the wearer from various activities such as laughing, singing, crying, arguing,
and sighing. These annotations are based on ground truth relevant for
psychologists who intend to monitor/infer the social context of individuals
coping with breast cancer. We pursued a case study on couples coping with
breast cancer to know how the conversations affect emotional and social well
being. In the state-of-the-art methods, psychologists and their team have to
hear the audio recordings for making these inferences by subjective evaluations
that not only are time-consuming and costly, but also demand manual data coding
for thousands of audio files. The BigEAR framework automates the audio
analysis. We computed the accuracy of BigEAR with respect to the ground truth
obtained from a human rater. Our approach yielded overall average accuracy of
88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International
Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27,
2016, Washington DC, US
DESIGN AND EVALUATION OF HARMONIC SPEECH ENHANCEMENT AND BANDWIDTH EXTENSION
Improving the quality and intelligibility of speech signals continues to be an important topic in mobile communications and hearing aid applications. This thesis explored the possibilities of improving the quality of corrupted speech by cascading a log Minimum Mean Square Error (logMMSE) noise reduction system with a Harmonic Speech Enhancement (HSE) system. In HSE, an adaptive comb filter is deployed to harmonically filter the useful speech signal and suppress the noisy components to noise floor. A Bandwidth Extension (BWE) algorithm was applied to the enhanced speech for further improvements in speech quality. Performance of this algorithm combination was evaluated using objective speech quality metrics across a variety of noisy and reverberant environments. Results showed that the logMMSE and HSE combination enhanced the speech quality in any reverberant environment and in the presence of multi-talker babble. The objective improvements associated with the BWE were found to be minima
The listening talker: A review of human and algorithmic context-induced modifications of speech
International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output
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