An Introduction to Audio Content Analysis: Applications in by Alexander Lerch PDF

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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 through a widely known professional within the box, this e-book offers easy accessibility to various research algorithms and permits comparability among various ways to a similar activity, making it worthy for novices to audio sign processing and specialists alike. A assessment of suitable basics in audio sign processing, psychoacoustics, and track concept, in addition to downloadable MATLAB records also are included.

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Chapter 1 creation (pages 1–5):
Chapter 2 basics (pages 7–30):
Chapter three prompt 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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Extra info for An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics

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3 Spectral rolloff for the three prototypical spectral shapes silence (zero magnitude at all bins),flat(same amplitude at all bins), and peak (all bins except one have zero magnitude) Spectral Shape silence flat mag. 4 shows the spectral rolloff for an example signal. It is comparably low in the presence of a tone and higher — although somewhat erratic — during the noise-filled pauses. 1 Common Variants Spectral bins representing very low or very high frequencies may in many cases be considered to be unnecessary or unwanted for the analysis.

Frequency Domain Compression In certain applications such as auditory processing it might be of interest to apply a nonlinear compression function to the magnitude spectrum before transforming it back. Tolonen and Karjalainen named such a combined approach the generalized ACF to be computed by (see Sect. 2, [28]) rßxxM=r1{\X{^)\ß}. 74) A value ß = 2 would result in the normal ACF. 7 Linear Prediction The idea of linear prediction is to use preceding (sample) values to estimate (or predict) future values.

Still the argument that ultimately every audio analysis task is about the extraction of perceivable features has merit as well. Auditory modeling is a lively subject of research; Lyon et al. review different approaches in [22]. 1 Gammatone Filterbank A widely used auditory filterbank is the so-called gammatone filterbank. 11 shows the impulse response of such a gammatone filter with the following parametrization: center frequency fc = 1 kHz, bandwidth Δ / = 125 Hz, and order O = 4. Slaney showed that gammatone filters can be efficiently implemented with cascaded second-order filters [25].

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An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics by Alexander Lerch

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