Music signal processing may appear to be the junior relation of the large and mature field of speech signal processing, not least because many techniques and representations originally developed for speech have been applied to music, often with good results. However, music signals possess specific acoustic and structural characteristics that distinguish them from spoken language or other nonmusical signals. This paper provides an overview of some signal analysis techniques that specifically address musical dimensions such as melody, harmony, rhythm, and timbre. We will examine how particular characteristics of music signals impact and determine these techniques, and we highlight a number of novel music analysis and retrieval tasks that such processing makes possible. Our goal is to demonstrate that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.

/pdf/signal-processing-for-music-analysis-1n1th5w5f8.pdf

Signal Processing for Music Analysis

We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.

https://repositori.upf.edu/bitstream/10230/27565/1/Urbano_ftir_mus.pdf

Music Information Retrieval: Recent Developments and Applications

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

An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics

Given a large audio database of music recordings, the goal of classical audio identification is to identify a particular audio recording by means of a short audio fragment. Even though recent identification algorithms show a significant degree of robustness towards noise, MP3 compression artifacts, and uniform temporal distortions, the notion of similarity is rather close to the identity. In this paper, we address a higher level retrieval problem, which we refer to as audio matching: given a short query audio clip, the goal is to automatically retrieve all excerpts from all recordings within the database that musically correspond to the query. In our matching scenario, opposed to classical audio identification, we allow semantically motivated variations as they typically occur in different interpretations of a piece of music. To this end, this paper presents an efficient and robust audio matching procedure that works even in the presence of significant variations, such as nonlinear temporal, dynamical, and spectral deviations, where existing algorithms for audio identification would fail. Furthermore, the combination of various deformation- and fault-tolerance mechanisms allows us to employ standard indexing techniques to obtain an efficient, index-based matching procedure, thus providing an important step towards semantically searching large-scale real-world music collections.

Efficient Index-Based Audio Matching

https://mediatum.ub.tum.de/doc/980066/file.pdf

A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams

We present an efficient and robust multiscale DTW (MsDTW) approach to music synchronization for time-aligning CD recordings of different interpretations of the same piece. The general strategy is to recursively project an alignment path computed at a coarse resolution level to the next higher level and then to refine the projected path. As main contributions, we address several crucial issues including the design and specification of robust and scalable audio features, suitable local cost measures, MsDTW levels, constraint regions, as well as sampling rate adaptation and structural enhancement strategies. Extensive experiments on Western classical music show that our MsDTW-based algorithm yields the same alignment result as the classical DTW-based strategy while significantly reducing the running time and memory requirements. Even for pieces of a duration of 10 to 15minutes, the alignment (based on previously extracted feature sequences) can be computed in less than a second.

/pdf/an-efficient-multiscale-approach-to-audio-synchronization-2qv3vuzv32.pdf

An Efficient Multiscale Approach to Audio Synchronization.

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An Efficient Multiscale Approach to Audio Synchronization.