publications

@article{lerch_libaca_2022-1,

title = {libACA, pyACA, and ACA-Code: Audio Content Analysis in 3 Languages},

author = {Alexander Lerch},

url = {https://www.sciencedirect.com/science/article/pii/S2665963822000677},

doi = {10.1016/j.simpa.2022.100349},

issn = {2665-9638},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-04},

journal = {Software Impacts},

pages = {100349},

abstract = {The three packages libACA, pyACA, and ACA-Code provide reference implementations for basic approaches and algorithms for the analysis of musical audio signals in three different languages: C++, Python, and Matlab. All three packages cover the same algorithms, such as extraction of low level audio features, fundamental frequency estimation, as well as simple approaches to chord recognition, musical key detection, and onset detection. In addition, it implementations of more generic algorithms useful in audio content analysis such as dynamic time warping and the Viterbi algorithm are provided. The three packages thus provide a practical cross-language and cross-platform reference to students and engineers implementing audio analysis algorithms and enable implementation-focused learning of algorithms for audio content analysis and music information retrieval.},

keywords = {Audio content analysis, C++, Matlab, music information retrieval, Python},

pubstate = {published},

tppubtype = {article}

}