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Titlebook: Intelligent Audio Analysis; Björn W. Schuller Book 2013 Springer-Verlag Berlin Heidelberg 2013 Audio Enhancement.Audio Robustness.Audio So

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书目名称Intelligent Audio Analysis
编辑Björn W. Schuller
视频video
概述Provides benchmark results and standardised test-beds for a broader range of audio analysis tasks.Extends the range of Intelligent Audio Analysis application by.showing its potential in new tasks.Stim
丛书名称Signals and Communication Technology
图书封面Titlebook: Intelligent Audio Analysis;  Björn W. Schuller Book 2013 Springer-Verlag Berlin Heidelberg 2013 Audio Enhancement.Audio Robustness.Audio So
描述This book provides the reader with the knowledge necessary for comprehension of the field of Intelligent Audio Analysis. It firstly introduces standard methods and discusses the typical Intelligent Audio Analysis chain going from audio data to audio features to audio recognition.  Further, an introduction to audio source separation, and enhancement and robustness are given. .After the introductory parts, the book shows several applications for the three types of audio: speech, music, and general sound. Each task is shortly introduced, followed by a description of the specific data and methods applied, experiments and results, and a conclusion for this specific task. .The books provides benchmark results and standardized test-beds for a broader range of audio analysis tasks. The main focus thereby lies on the parallel advancement of realism in audio analysis, as too often today’s results are overly optimistic owing to idealized testing conditions, and it serves to stimulate synergies arising from transfer of methods and leads to a holistic audio analysis.
出版日期Book 2013
关键词Audio Enhancement; Audio Robustness; Audio Source Separation; Fully Automatic Chunking; Music Informatio
版次1
doihttps://doi.org/10.1007/978-3-642-36806-6
isbn_softcover978-3-642-44277-3
isbn_ebook978-3-642-36806-6Series ISSN 1860-4862 Series E-ISSN 1860-4870
issn_series 1860-4862
copyrightSpringer-Verlag Berlin Heidelberg 2013
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Audio FeaturesThese parameters describe properties of the audio usually in a highly information reduced form and typically at a considerably lower rate, such as the mean energy or pitch over a longer period of time. As different Intelligent Audio Analysis tasks are often best represented by different such ’featur
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Applications in Intelligent Speech Analysisl technical and computer systems, once they are able to recognise speech as well as humans do—be it for interaction purposes with humans, mediation purposes between humans, or speech retrieval. Here, state-of-the-art methodology is presented for highly robust speech recognition, nonlinguistic vocali
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Applications in Intelligent Music Analysisnce. From this exciting field, recent application examples were selected for presentation in detail from the work of the author including current performance benchmarks. These comprise drum-beat separation, onset detection, tempo, metre, ballroom dance style, and mood determination, key and chord re
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Discussion distilled ’best practice’ recommendation is given to the reader. This includes aspects of high realism, standardised, multi-faceted and machine-aided data collection, source separation, feature brute-forcing, temporal evolution modelling, coupling of tasks, and standardisation. Then, a critical dis
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