publications

@book{lerch_introduction_2012,

title = {An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics},

author = {Alexander Lerch},

url = {http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266785},

isbn = {978-1-118-26682-3},

year  = {2012},

date = {2012-01-01},

publisher = {Wiley-IEEE Press},

address = {Hoboken},

abstract = {With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included. Please visit the companion website: www.AudioContentAnalysis.org},

keywords = {analysis, audio, audio signal processing, information, listening, machine, machine listening, music, music analysis, music information retrieval, processing, retrieval, signal},

pubstate = {published},

tppubtype = {book}

}

@book{lerch_software-based_2009,

title = {Software-Based Extraction of Objective Parameters from Music Performances},

author = {Alexander Lerch},

url = {http://dx.doi.org/10.14279/depositonce-2025},

isbn = {978-3-640-29496-1},

year  = {2009},

date = {2009-01-01},

publisher = {GRIN Verlag},

address = {M\"{u}nchen},

abstract = {Different music performances of the same score may significantly differ from each other. It is obvious that not only the composer’s work, the score, defines the listener’s music experience, but that the music performance itself is an integral part of this experience. Music performers use the information contained in the score, but interpret, transform or add to this information. Four parameter classes can be used to describe a performance objectively: tempo and timing, loudness, timbre and pitch. Each class contains a multitude of individual parameters that are at the performers’ disposal to generate a unique physical rendition of musical ideas. The extraction of such objective parameters is one of the difficulties in music performance research. This work presents an approach to the software-based extraction of tempo and timing, loudness and timbre parameters from audio files to provide a tool for the automatic parameter extraction from music performances. The system is applied to extract data from 21 string quartet performances and a detailed analysis of the extracted data is presented. The main contributions of this thesis are the adaptation and development of signal processing approaches to performance parameter extraction and the presentation and discussion of string quartet performances of a movement of Beethoven’s late String Quartet op. 130.},

keywords = {analysis, audio, content, information, music, performance, retrieval},

pubstate = {published},

tppubtype = {book}

}