Marsyas: A framework for audio analysis (original) (raw)

NewsComm: a hand-held interface for interactive access to structured audio

Proceedings of the SIGCHI conference on Human factors in computing systems common ground - CHI '96, 1996

The NewsComm system delivers personalized news ~nd other program material as audio to mobile users through a hand-held playback device. This paper focuses on the iterative design and user testing of the hand-held interface. The interface was ftrst designed and tested in a softwareonly enviroument and then ported to a custom hardware platform. The hand-held device enables navigation through audio recordings based on structural information which is extracted from the audio using digital signal processing techniques. The interface design ~00resses the problems of designing a hand-held and primarily non-visual interface for accessing large amounts of StlUCtured audio recordings.

Iracema: a Python library for audio content analysis

Anais do Simpósio Brasileiro de Computação Musical (SBCM 2019), 2019

This paper introduces the alpha version of a Python library called Iracema, which aims to provide models for the extraction of meaningful information from recordings of monophonic pieces of music, for purposes of research in music performance. With this objective in mind, we propose an architecture that will provide to users an abstraction level that simplifies the manipulation of different kinds of time series, as well as the extraction of segments from them. In this paper we: (1) introduce some key concepts at the core of the proposed architecture; (2) list the current functionalities of the package; (3) give some examples of the application programming interface; and (4) give some brief examples of audio analysis using the system.

Reusable metadata and software components for automatic audio analysis

2009

Content-based metadata is becoming increasingly important for managing audio collections in digital library applications. While Music Information Retrieval (MIR) research provides means for extracting metadata from audio recordings, no common practice emerges for representing analysis results or exchanging algorithms. This paper argues for the need of modularity through interoperable components and data publishing methods in MIR applications. We demonstrate the use of a common API for audio analysis, enhanced with easily extended Semantic Web ontologies for describing results and configuration. Built on the extensible ontological framework provided by the Music Ontology[1], our system allows for the representation of diverse information such as musical facts, features or analysis parameters in a uniform, reusable and machine interpretable format. Our demonstration will be using SAWA 1 , a Web-application available 2 for researchers interested in these technologies.

Manipulation, analysis and retrieval systems for audio signals

2002

Abstract Digital audio and especially music collections are becoming a major part of the average computer user experience. Large digital audio collections of sound effects are also used by the movie and animation industry. Research areas that utilize large audio collections include: Auditory Display, Bioacoustics, Computer Music, Forensics, and Music Cognition. In order to develop more sophisticated tools for interacting with large digital audio collections, research in Computer Audition algorithms and user interfaces is required.

A framework for audio analysis based on classification and temporal segmentation

1999

Abstract Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using current techniques. Alternative solutions are semi-automatic user interfaces that let users interact with sound in flexible ways based on content.

Automatic annotation of musical audio for interactive applications

2006

As machines become more and more portable, and part of our everyday life, it becomes apparent that developing interactive and ubiquitous systems is an important aspect of new music applications created by the research community. We are interested in developing a robust layer for the automatic annotation of audio signals, to be used in various applications, from music search engines to interactive installations, and in various contexts, from embedded devices to audio content servers. We propose adaptations of existing signal processing techniques to a real time context. Amongst these annotation techniques, we concentrate on low and mid-level tasks such as onset detection, pitch tracking, tempo extraction and note modelling. We present a framework to extract these annotations and evaluate the performances of different algorithms. The first task is to detect onsets and offsets in audio streams within short latencies. The segmentation of audio streams into temporal objects enables vario...

Semantic Audio Analysis Utilities and Applications

Extraction, representation, organisation and application of metadata about audio recordings are in the concern of semantic audio analysis. Our broad interpretation, aligned with recent developments in the field, includes methodological aspects of semantic audio, such as those related to information management, knowledge representation and applications of the extracted information. In particular, we look at how Semantic Web technologies may be used to enhance information management practices in two audio related areas: music informatics and music production.

Audio Information Retrieval (AIR) Tools

2000

Most of the work in music Information Retrieval (MIR) and analysis has been performed using symbolic representation like MIDI. The recent advances in computing power and network connectivity have made large amounts of raw digital audio data available in the form of unstructured monolithic sound files. In this work the focus is on tools that work directly on real world audio data without attempting to transcribe the music. To distinguish from symbolic−based music IR for the remainder of the paper we use the term audio IR (AIR) to refer to techniques that work directly on raw audio signals. Obviously these signals can contain music as well as other types of audio like speech. We describe a series of tools based on current and newly developed techniques for AIR integrated under MARSYAS, our framework for audio analysis. For related work refer to (Foote, 1999). The tools developed are based on Signal Processing, Pattern Recognition and Visualization techniques. Finally, due to the immat...

Guidage: a Fast audio Query Guided assemblage

2007

In this article, a method is proposed for fast and automatic retrieval of factors of audio content in a large audio database based on user's audio query. The proposed method, unlike most existing systems, takes explicit considerations of temporal morphology of audio content. This work touches upon several existing approaches and technologies for sound manipulations, such as sound texture synthesis, music and audio mosaicing on the synthesis side, and audio matching, query by audio and audio structure discovery on the analysis side. Destined for creative applications, the proposed method is modular by allowing interactive choice of search criteria. The analysis side of the proposed model features a new audio structure discovery algorithm called Audio Oracle that describes the temporal morphologies of the underlying sound as a compact state-space model. The search engine, and the main focus of this paper, features a fast and novel algorithm based on dynamic programming called Guid...