There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.

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Musical genre classification of audio signals

The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.

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Content-Based Music Information Retrieval: Current Directions and Future Challenges

Les approches utilisees pour la recherche d'information musicale (RIM) sont multidisciplinaires : bibliotheconomie et science de l'information, musicologie, theorie musicale, ingenierie du son, informatique, droit et commerce... L'article vise a identifier et a expliquer la problematique de de la RIM alors qu'elle devient une discipline a part entiere, les influences historiques, l'etat-de-l'art de la recherche et les solutions potentielles. L'information musicale est multifacette - ton, temporalite, harmonie, timbre, edition, texte et bibliographie - , l'acces a chacune de ces facettes constituant un defi pour la recherche et le developpement. Mais la RIM represente egalement un defi multirepresentationnel, multiculturel, multiexperience et multidisciplinaire. Les systemes de RIM deploient differents degres d'exhaustivite representationnelle. Ils relevent generalement de deux types : les systemes analytiques ou de production, et les systemes de localisation. En conclusion, l'A. mentionne quelques ateliers et symposiums recents ainsi que les principaux projets de recherche concernant la RIM.

Music Information Retrieval.

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.

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pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

An important application for use with multimedia databases is a browsing aid, which allows a user to quickly and efficiently preview selections from either a database or from the results of a database query. Methods for facilitating browsing, though, are necessarily media dependent. We present one such method that produces short, representative samples (or "audio thumbnails") of selections of popular music. This method attempts to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. A reduced spectral representation of the selection based on a chroma transformation of the spectrum is used to find repeating patterns. This representation encodes harmonic relationships in a signal and thus is ideal for popular music, which is often characterized by prominent harmonic progressions. The method is evaluated over a sizable database of popular music and found to perform well, with most of the errors resulting from songs that do not meet our structural assumptions.

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To catch a chorus: using chroma-based representations for audio thumbnailing

With the growing prevalence of large databases of multimedia content, methods for facilitating rapid browsing of such databases or the results of a database search are becoming increasingly important. However, these methods are necessarily media dependent. We present a system for producing short, representative samples (or "audio thumbnails") of selections of popular music. The system searches for structural redundancy within a given song with the aim of identifying something like a chorus or refrain. To isolate a useful class of features for performing such structure-based pattern recognition, we present a development of the chromagram, a variation on traditional time-frequency distributions that seeks to represent the cyclic attribute of pitch perception, known as chroma. The pattern recognition system itself employs a quantized chromagram that represents the spectral energy at each of the 12 pitch classes. We evaluate the system on a database of popular music and score its performance against a set of "ideal" thumbnail locations. Overall performance is found to be quite good, with the majority of errors resulting from songs that do not meet our structural assumptions.

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Audio thumbnailing of popular music using chroma-based representations

In this paper, we present a spectrum-based system for singer identification that operates for the ideal case in which audio samples contain only the singer's voice. Our method begins with the computation of a robust estimate of the spectral envelope called the composite transfer function (CTF). The CTF is derived from the instantaneous amplitude and frequency of the sinusoidal partials which make up the vocal signal. Unlike traditional source-filter theory , the CTF does not explicitly separate the spectral characteristics of the vocal source and the vocal tract filter. The principal components of the CTFs are used as features for a quadratic classifier to identify singers. The approach is validated on a database containing samples from twelve classically trained singers. In cross validation experiments, test set accuracies of approximately 95% are found for a baseline case. The classifier's performance is not degraded when different vowels are included in classifier training and evaluation. Restricting the frequency range of the CTFs and using a test set containing samples extracted from solo performances of Italian arias reduces the test set accuracy to 70-80%.

Singing voice identification using spectral envelope estimation

Time series representations are common in MIR applications such as query-by-humming, where a sung query might be represented by a series of ‘notes’ for database retrieval. While such a transcription into a sequence of (pitch, duration) pairs is convenient and musically intuitive, there is no evidence that it is an optimal representation. The present work explores three time series representations for sung queries: a sequence of notes, a ‘smooth’ pitch contour, and a novel sequence of pitch histograms. Dynamic alignment procedures are described for the three representations. Multiple continuity constraints are explored and a modified dynamic alignment procedure is described for the histogram representation. We measure the performance of the three representations using a collection of naturally sung queries applied to a target database of varying size. The results show that the note representation lends itself to rapid retrieval whereas the contour representation lends itself to robust performance. The histogram representation yields performance nearly as robust as the contour representation, but with computational complexity similar to the note representation.

Time Series Alignment for Music Information Retrieval.

Much research in music information retrieval has focused on query-by-humming systems, which search melodic databases using sung queries. The database retrieval aspect of such systems has received considerable attention, but query processing and the melodic representation have not been examined as carefully. Common methods for query processing are based on musical intuition and historical momentum rather than specific performance criteria; existing systems often employ rudimentary note segmentation or coarse quantization of note estimates. In this work, we examine several alternative query processing methods as well as quantized melodic representations. One common difficulty with designing query-by-humming systems is the coupling between system components. We address this issue by measuring the performance of the query processing system both in isolation and coupled with a retrieval system. We first measure the segmentation performance of several note estimators. We then compute the retrieval accuracy of an experimental query-by-humming system that uses the various note estimators along with varying degrees of pitch and duration quantization. The results show that more advanced query processing can improve both segmentation performance and retrieval performance, although the best segmentation performance does not necessarily yield the best retrieval performance. Further, coarsely quantizing the melodic representation generally degrades retrieval accuracy.

M.A. Bartsch

Papers

To catch a chorus: using chroma-based representations for audio thumbnailing

Audio thumbnailing of popular music using chroma-based representations

Singing voice identification using spectral envelope estimation

Time Series Alignment for Music Information Retrieval.

Note segmentation and quantization for music information retrieval