A Novel Hybrid Approach for Retrieval of the Music Information

Proceedings of Meetings on Acoustics, 2019

The exponential growth of computer processing power, cloud data storage, and the crowdsourcing model of gathering data usher new possibilities to the Music Information Retrieval (MIR) field. MIR does not exclusively focus on the retrieval of music content; the field also involves discovering the feelings and emotions expressed in music, incorporating other modalities to help with this issue, user profiling, merging music with social media, and offering qualitative music service recommendations. Moreover, 5G telecommunications networks, characterized by 'near-instant and everything in the vicinity talks with one another,' with exponentially faster download and upload speeds, may change the existing models and create a new age of interconnectedness.

ACM Computing Surveys, 2018

A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the...

Journal of Intelligent Information Systems

Increasing availability of music data via Internet evokes demand for efficient search through music files. Users' interests include melody tracking, harmonic structure analysis, timbre identification, and so on. We visualize, in an illustrative example, why content based search is needed for music data and what difficulties must be overcame to build an intelligent music information retrieval system.

Data analysis, machine learning and …, 2007

We present MIRToolbox, an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features related, among others, to timbre, tonality, rhythm or form. The objective is to offer a state of the art of computational approaches in the area of Music Information Retrieval (MIR). The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches -including new strategies we have developed -, that users can select and parametrize. These functions can adapt to a large area of objects as input.

In this study, the notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, in order to understand the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The selected perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic (MIDI) and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features ...

2006

Two main groups of Music Information Retrieval (MIR) systems for content-based searching can be distinguished, systems for searching audio data and systems for searching notated music. There are also hybrid systems that first transcribe audio signal into a symbolic description of notes and then search a database of notated music. An example of such music transcription is the work of Klapuri , which in particular is concerned with multiple fundamental frequency estimation, and musical metre estimation, which has to do with ordering the rhythmic aspects of music. Part of the work is based on known properties of the human auditory system. Content-based music search systems can be useful for a variety of purposes and audiences:

Multimodal Music Processing (Schloss Dagstuhl, Germany, 2012), M. Müller and M. Goto, Eds., vol. Seminar, 2012

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing.

Dagstuhl Seminar Proceedings, 2006

Abstract. Search and retrieval of specific musical content such as emotive or sonic features has become an important aspect of Music Information Retrieval system development, but only little research is user-oriented. We summarize results of an elaborate user-study that explores ...

2018

Music is a rich harmonic audio signal with variety of forms and musical dimensions. The huge canvas of music evolved through centuries and decades involves variety of music genres and features. Different musical data representation, storage meth- ods and feature classification approaches helps to understand diversities and dimensions in musical features.This paper covers music data representation methods, musical features, feature engineering, generation, selection and learning methodologies used for musical data with example application for query by humming. The example chosen uses generic music features considering no need of familiarity with specific music genre. De- tailed discussion is provided for feature generation process with different approaches used. These feature engineering examples in music data analytics are useful in various applications of content based music information retrieval such as query by humming, similarity of music, clustering, music plagiarism etc. Enorm...

Lecture Notes in Computer Science, 2015

Music Information Retrieval (MIR) is an interdisciplinary research area that covers automated extraction of information from audio signals, music databases and services enabling the indexed information searching. In the early stages the primary focus of MIR was on music information through Query-by-Humming (QBH) applications, i.e. on identifying a piece of music by singing (singing/whistling), while more advanced implementations supporting Queryby-Example (QBE) searching resulted in names of audio tracks, song identification, etc. Both QBH and QBE required several steps, among others an optimized signal parametrization and the soft computing approach. Nowadays, MIR is associated with research based on the content analysis that is related to the retrieval of a musical style, genre or music referring to mood or emotions. Even though, this type of music retrieval called Query-by-Category still needs feature extraction and parametrization optimizing, but in this case search of global online music systems and services applications with their millions of users is based on statistical measures. The paper presents details concerning MIR background and answers a question concerning usage of soft computing versus statistics, namely: why and when each of them should be employed.

Proceedings of the seventh ACM international conference on Multimedia (Part 1) - MULTIMEDIA '99, 1999

The increasing availability of music in digital format needs to be matched by the development of tools for music accessing, filtering, classification, and retrieval. The research area of Music Information Retrieval (MIR) covers many of these aspects. The aim of this paper is to present an overview of this vast and new field. A number of issues, which are peculiar to the music language, are described-including forms, formats, and dimensions of music-together with the typologies of users and their information needs. To fulfil these needs a number of approaches are discussed, from direct search to information filtering and clustering of music documents. An overview of the techniques for music processing, which are commonly exploited in many approaches, is also presented. Evaluation and comparisons of the approaches on a common benchmark are other important issues. To this end, a description of the initial efforts and evaluation campaigns for MIR is provided.

Personalized and user-aware systems for retrieving multimedia items are becoming increasingly important as the amount of available multimedia data has been spiraling. A personalized system is one that incorporates information about the user into its data processing part (e.g., a particular user taste for a movie genre). A context-aware system, in contrast, takes into account dynamic aspects of the user context when processing the data (e.g., location and time where/when a user issues a query). Today's user-adaptive systems often incorporate both aspects.

The digital revolution has brought about a massive increase in the availability and distribution of music-related documents of various modalities comprising textual, audio, as well as visual material. Therefore, the development of techniques and tools for organizing, structuring, retrieving, navigating, and presenting music-related data has become a major strand of research—the field is often referred to as Music Information Retrieval (MIR). Major challenges arise because of the richness and diversity of music in form and content leading to novel and exciting research problems. In this article, we give an overview of new developments in the MIR field with a focus on content-based music analysis tasks including audio retrieval, music synchronization, structure analysis, and performance analysis.

1997

This paper describes a system designed to retrieve melodies from a database on the basis of a few notes sung into a microphone. The system first accepts acoustic input from the user, transcribes it into common music notation, then searches a database of 9400 folk tunes for those containing the sung pattern, or patterns similar to the sung pattern; retrieval is ranked according to the closeness of the match. The paper presents an analysis of the performance of the system using different search criteria involving melodic contour, musical intervals and rhythm; tests were carried out using both exact and approximate string matching. Approximate matching used a dynamic programming algorithm designed for comparing musical sequences. Current work focuses on developing a faster algorithm.