ruohoruotsi / pyACA

IN WORK: Python scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnlysis.org)

pyACA

Python scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

This package comprises implementations of simple algorithms and features for audio content analysis.

Please note that the provided code examples are only intended to showcase algorithmic principles – they are not suited to be used without parameter optimization and additional algorithmic tuning. Furthermore, the python code might violate typical python style conventions in order to be consistent with the Matlab code at https://github.com/alexanderlerch/ACA-Code

The majority of these python sources require the numpy and scipy packages installed. Several functions (such as MFCCs and Gammatone filters) are based on implementations in Slaney’s Auditory Matlab Toolbox.

Please feel free to visit http://www.audiocontentanalysis.org/code/ to find links to the latest versions of this code or to submit comments or code that fixes, improves and adds functionality.

The top-level functions are:

• computeFeature: calculates instantaneous features
• computePitch: calculates a fundamental frequency estimate
• computeKey: calculates a simple key estimate
• computeNoveltyFunction: simple onset detection
• computeBeatHisto: calculates a simple beat histogram

The names of the additional functions follow the following conventions:

• Feature*: instantaneous features
• Pitch*: pitch tracking approach
• Novelty*: novelty function computation
• Tool*: additional help functions such as frequency scale conversion, dynamic time warping, gammatone filterbank, ...

Example 1: Computation and plot of the Spectral Centroid

	import pyACA
	import matplotlib.pyplot as plt 

	# file to analyze
	cPath = "c:/temp/test.wav"

	# extract feature
	[v,t] = pyACA.computeFeatureCl(cPath, "SpectralCentroid")

	# plot feature output
	plt.plot(t,np.squeeze(v))

Example 2: Computation of multiple features (here: Spectral Centroid and Spectral Flux)

	import pyACA
	
	# read audio file
	cPath = "c:/temp/test.wav"
	[f_s,afAudioData] = pyACA.ToolReadAudio(cPath)

	# compute feature
	[vsc,t] = pyACA.computeFeature("SpectralCentroid", afAudioData, f_s)
	[vsf,t] = pyACA.computeFeature("SpectralFlux", afAudioData, f_s)