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An Introduction To Audio Content Analysis: Musi...
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Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.
The 2nd edition of An Introduction to Audio Content Analysis has been released. Similar to the 1st edition, it introduces standard base-line approaches to a variety of audio analysis and music information retrieval tasks and provides the reader with a multitude of pointers and references if interested. The second edition of the book is restructured, modernized, and expanded and serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio.
Alexander Lerch is Associate Professor at the Georgia Institute of Technology, where he works on the design and implementation of algorithms for audio content analysis and music information retrieval. Lerch is author of more than 60 peer-reviewed publications on a wide range of topics in audio and music analysis and processing.Before he joined the faculty at Georgia Tech, he co-founded the company zplane.development, a research-driven company which is a leading provider of advanced music software technology.
Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references. About the Author Alexander Lerch, PhD, is an Associate Professor at the Center for Music Technology, Georgia Institute of Technology. His research focuses on signal processing and machine learning applied to music, an interdisciplinary field commonly referred to as music information retrieval. He has authored more than 50 peer-reviewed publications and his website, www.AudioContentAnalysis.org, is a popular resource on Audio Content Analysis, providing video lectures, code examples, and other materials. Permissions Request permission to reuse content from this site
Adjective-noun pairs can elicit a subjective meaning of the audio to the listener, defined by an adjective that shapes the natural and social environment [20]. Moreover, the subjectivity can cover other areas such as affective dimensions as explored by the authors of [18] where they collected the International Affective Digitized Sounds (IADS) dataset consisting of 111 sounds without enforcing a subjective word in the label. The sounds were presented to participants who had to categorize them into one of five classes: happiness, anger, sadness, fear, and disgust. Results showed how participants have consistent trends categorizing these sounds, suggesting a relationship between adjectives and audio content.
Investigation of adjectives and verbs as qualifiers of perceptual categories has been successfully approached in other fields. In computer vision, Borth et al. [27] introduced the VisualSentiBank to perform sentiment analysis of images [28] based on adjective-noun pairs. In video analysis, actions described by verbs have been widely explored as described in these surveys [29, 30]. In text and language processing, authors in [31] introduced SentiWordnet to perform opinion mining using adjectives. In the music domain, acoustic characteristics and lyrics have been combined to detect sentiment in [32]. It is therefore to be expected that similar exploration of audio concepts will reveal to what extent we can automatically identify such qualifying information and how it could be combined for analysis of subjectivity in multimedia content [33, 34]. 59ce067264