By Alexander Lerch

ISBN-10: 111826682X

ISBN-13: 9781118266823

ISBN-10: 1118393554

ISBN-13: 9781118393550

With the proliferation of electronic audio distribution over electronic media, audio content material research is quickly changing into a demand for designers of clever signal-adaptive audio processing structures. Written by way of a widely known specialist within the box, this e-book offers easy accessibility to various research algorithms and permits comparability among assorted techniques to an identical activity, making it precious for beginners to audio sign processing and specialists alike. A overview of appropriate basics in audio sign processing, psychoacoustics, and track conception, in addition to downloadable MATLAB records also are included.

Please stopover at the significant other web site: www.AudioContentAnalysis.org

Content:
Chapter 1 advent (pages 1–5):
Chapter 2 basics (pages 7–30):
Chapter three on the spot beneficial properties (pages 31–69):
Chapter four depth (pages 71–78):
Chapter five Tonal research (pages 79–117):
Chapter 6 Temporal research (pages 119–137):
Chapter 7 Alignment (pages 139–150):
Chapter eight Musical style, Similarity, and temper (pages 151–162):
Chapter nine Audio Fingerprinting (pages 163–167):
Chapter 10 tune functionality research (pages 169–179):

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Additional resources for An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics

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The computation of the ACF in the time domain can thus be replaced by a simple multiplication in the frequency domain preceded and followed by an FT and an inverse FT, respectively. For long blocks this will save computing cycles and reduce computational workload when using an FFT algorithm. Note that with blocks of sampled data the result will be a circular ACF if the transformed block of data has not been correctly padded with zeros before transforming it. More specifically, computing the CCF of two blocks of length K.

Phase Difference Using Transforms An alternative to computing the phase difference directly is to use two different windows for computing two STFTs of one analysis block. Then, the instantaneous frequency can be computed by s , n „ (XD(k,n)-X*(k,n)\ e, //W1X un(ktn)=U(k) + 3[ \x^n)ir'\ (2-61) with Xo(k, n) being the STFT computed using the derivative of the window used for the calculation of X(k, n) [18]. This approach is independent of the hop size but requires the computation of two DFTs per block.

A survey of different gammatone filter implementations can be found in [26]. 12 shows the frequency (magnitude) response of a gammatone filterbank as computed by the Auditory Toolbox [27] for 20 bands between 100 Hz and 24 kHz. 6 Correlation Function The correlation function can be used to compute the similarity between two signals at a lag η. 66) Assuming the input signals are only blocks of K. = ie(n) — i8(n) + 1 samples, we can imagine an infinite number of zeros to the right and to the left of the actual samples; Eq.

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