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

By Alexander Lerch

With the proliferation of electronic audio distribution over electronic media, audio content material research is quick changing into a demand for designers of clever signal-adaptive audio processing platforms. Written via a well known specialist within the box, this e-book presents easy accessibility to assorted research algorithms and permits comparability among diverse techniques to an identical job, making it important for novices to audio sign processing and specialists alike. A evaluate of correct basics in audio sign processing, psychoacoustics, and song thought, in addition to downloadable MATLAB documents also are included.

Please stopover at the spouse site: www.AudioContentAnalysis.org

Content:
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 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

Sample text

See Sect. 2), ■ spectral shape: features describing the shape of the (magnitude spectrum of the) STFT (see Sect. 3), technical/signal properties: features that describe specific technical properties of the signal and cannot be categorized in other domains (see Sect. 4), and ■ intensity properties: features closely related to the amplitude or intensity of the audio signal such as volume and loudness (see Chap. 4). 1 Audio Pre-Processing The raw audio data is frequently pre-processed before computing instantaneous features from the data.

2 Note that there exist other approaches to defining the integration time that may differ significantly. 31) meaning that the Fourier Transform (FT) $ (see Sect. 3) of a time-reversed signal equals the complex-conjugate of the transform of the original signal. The flip function is a tool which helps us in the derivation: flip (x(i)) = x(-i). 34) = h(-i) * (h(i) * x(i)). 2 \H(jcj)\2 ■ Χ()ω). 37) Block-Based Processing Signal processing algorithms usually work block-based, meaning that the input signal is being split into consecutive blocks of frames with length AC.

51) with h! being a periodically extended sequence of the signal block with the sampie boundaries is(n) and ie(n). ■ Time and Frequency Shifting 3{x(i -io)} 3"1 {X(k- k0,n)} = X(k,n) = x(is(n) · exp ( - j 2nk. ie(n)) •exp(j|V). 55) ■ Duality 3{X(i)} = ^x(k,n). 56) SIGNAL PROCESSING 23 Explicit Phase Difference The derivation operation from Eq. 46) can be easily approximated by computing the difference of the phases of consecutive STFT phases: ui(k,n) = ΔΦΜ(<;,η) — /s. 57) The actual computation, however, is not as trivial as it seems at first glance as Au(fc, n) represents the unwrapped phase difference while the computed phase spectrum gives the wrapped phase in the range of ] — π; π].

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