By Alexander Lerch

With the proliferation of electronic audio distribution over electronic media, audio content material research is quickly turning into a demand for designers of clever signal-adaptive audio processing structures. Written through a well known specialist within the box, this booklet presents easy access to various research algorithms and permits comparability among diverse methods to an identical activity, making it important for newbies to audio sign processing and specialists alike. A assessment of correct basics in audio sign processing, psychoacoustics, and track idea, in addition to downloadable MATLAB records also are included.

Please stopover at the better half web site: www.AudioContentAnalysis.org

Content:
Chapter 1 creation (pages 1–5):
Chapter 2 basics (pages 7–30):
Chapter three prompt positive factors (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 song functionality research (pages 169–179):

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

Example text

This should be a good measure of the signal's range while discarding infrequent outliers, namely the upper and lower 5%. 0)) - max (Q x (0)). 3 Spectral Shape Most of the features describing the spectral shape of an audio signal are closely related to the timbre of this signal. The timbre of a sound is referred to as its sound color, its quality, or its texture. Besides pitch and loudness, timbre is considered as "the third attribute of the subjective experience of musical tones" [37]. Timbre can be explained by two closely related phenomena, which will be referred to as timbre quality and timbre identity.

60) To improve the reliability of the phase unwrapping process the hop size H should be as small as possible. 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].

3 on the spectral centroid. 6 Variance and Standard Deviation Both the variance and the standard deviation measure the spread of the input signal x(i) around its arithmetic mean. The variance σ2χ is defined by σ χ(») = ^ Σ (*(*)-M*(n)) 2 · (3-16) Strictly speaking this is the so-called biased estimate of the variance in contrast to the unbiased estimate

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