Metadata-Version: 2.1
Name: pyACA
Version: 0.2.1
Summary: scripts accompanying the book An Introduction to Audio Content Analysis by Alexander Lerch
Home-page: https://github.com/alexanderlerch/pyACA
Author: Alexander Lerch
Author-email: info@AudioContentAnalysis.org
License: MIT
Description: # 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_
        
        ```python
        	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_)
        
        ```python
        	import pyACA
        	import matplotlib.pyplot as plt 
        
        	# 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)
        
        ```
        
        
        
Keywords: audio analysis features pitch key extraction music onset beat detection descriptors
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Description-Content-Type: text/markdown
