Automatic Audio Content Analysis

This paper describes the theoretic framework and applications of automatic audio content analysis. Research in multimedia content analysis has so far concentrated on the video domain. We demonstrate the strength of automatic audio content analysis. We explain the algorithms we use, including analysis of amplitude, frequency and pitch, and simulations of human audio perception. These algorithms serve us as tools for further audio content analysis. We use these tools in applications like the segmentation of audio data streams into logical units for further processing, the analysis of music, as well as the recognition of sounds indicative of violence like shots, explosions and cries

Physically inspired interactive music machines: making contemporary composition accessible?

Much of what we might call "high-art music" occupies the difficult end of listening for contemporary audiences. Concepts such as pitch, meter and even musical instruments often have little to do with such music, where all sound is typically considered as possessing musical potential. As a result, such music can be challenging to educationalists, for students have few familiar pointers in discovering and understanding the gestures, relationships and structures in these works. This paper describes on-going projects at the University of Hertfordshire that adopt an approach of mapping interactions within visual spaces onto musical sound. These provide a causal explanation for the patterns and sequences heard, whilst incorporating web interoperability thus enabling potential for distance learning applications. While so far these have mainly driven pitch-based events using MIDI or audio files, it is hoped to extend the ideas using appropriate technology into fully developed composition tools, aiding the teaching of both appreciation/analysis and composition of contemporary music

Enhancing Audio Signal Quality and Learning Experience with Integrated Covariance Weiner Filtering in College Music Education

In recent years, computer music technology has become increasingly prevalent in college music education, offering new possibilities for creative expression and pedagogical approaches. This paper concentrated on the music education in the colleges with the application of integrated time and frequency filtering (ITFF) with Kalman integrated covariance Weiner filtering in college music education. The ITFF technique combines time and frequency domain analysis to enhance the quality and clarity of audio signals. By integrating the Kalman integrated covariance Weiner filtering, the ITFF method provides robust noise reduction and improved signal representation. This integrated approach enables music educators to effectively analyze and manipulate audio signals in real-time, fostering a more immersive and engaging learning environment for students. The findings of this study highlight the benefits and potential applications of ITFF with Kalman-integrated covariance Weiner filtering in college music education, including audio signal enhancement, sound synthesis, and interactive performance systems. The integration of computer music technology with advanced filtering techniques presents new opportunities for exploring sound, composition, and music production within an educational context

Semantic Audio Analysis Utilities and Applications.

PhDExtraction, 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. In the first area, we are concerned with music information retrieval (MIR) and related research. We examine how structured data may be used to support reproducibility and provenance of extracted information, and aim to support multi-modality and context adaptation in the analysis. In creative music production, our goals can be summarised as follows: O↵-the-shelf sound editors do not hold appropriately structured information about the edited material, thus human-computer interaction is inefficient. We believe that recent developments in sound analysis and music understanding are capable of bringing about significant improvements in the music production workflow. Providing visual cues related to music structure can serve as an example of intelligent, context-dependent functionality. The central contributions of this work are a Semantic Web ontology for describing recording studios, including a model of technological artefacts used in music production, methodologies for collecting data about music production workflows and describing the work of audio engineers which facilitates capturing their contribution to music production, and finally a framework for creating Web-based applications for automated audio analysis. This has applications demonstrating how Semantic Web technologies and ontologies can facilitate interoperability between music research tools, and the creation of semantic audio software, for instance, for music recommendation, temperament estimation or multi-modal music tutorin

Introducing CatOracle: Corpus-based concatenative improvisation with the Audio Oracle algorithm

CATORACLE responds to the need to join high-level control of audio timbre with the organization of musical form in time. It is inspired by two powerful existing tools: CataRT for corpus-based concatenative synthesis based on the MUBU for MAX library, and PYORACLE for computer improvisation, combining for the first time audio descriptor analysis and learning and generation of musical structures. Harnessing a user-defined list of audio fea- tures, live or prerecorded audio is analyzed to construct an “Audio Oracle” as a basis for improvisation. CatOracle also extends features of classic concatenative synthesis to include live interactive audio mosaicking and score-based transcription using the BACH library for MAX. The project suggests applications not only to live performance of written and improvised electroacoustic music, but also computer-assisted composition and musical analysis