The measurement of meaning

Digital processing of speech signals

The yield of alkenols and cycloalkenols is substantially improved by carrying out the reaction of olefins with formaldehyde in the presence of selected catalysts. In accordance with one embodiment, alk-3-en-1-ols are produced in good yields from isobutylene and formaldehyde in the presence of organic carboxylic acid salts of Group IB metals.

The Organization of the Cochlear Receptor

A system that performs analysis and comparison of audio data files based upon the content of the data files is presented. The analysis of the audio data produces a set of numeric values (a feature vector) that can be used to classify and rank the similarity between individual audio files typically stored in a multimedia database or on the World Wide Web. The analysis also facilitates the description of user-defined classes of audio files, based on an analysis of a set of audio files that are members of a user-defined class. The system can find sounds within a longer sound, allowing an audio recording to be automatically segmented into a series of shorter audio segments.

Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information

Emotion and Meaning in Music

Foundations of musical signal processing innovations in musical signal processing musical signal processing composition and signal processing.

Musical Signal Processing

Fourteen articles by different authors describe digital audio and computer music systems made possible by advances in digital signal processing theory, hardware design, and programming techniques. They focus on models that combine time-domain and frequency-domain representations (grains, wavelets,

Representations of musical signals

In the research on timbre, two important variables have to be assigned from the onset: the instruments used to analyze and to model the physical sound, and the techniques employed to provide an efficient and manageable representation of the data. The experimental methodology which results from these choices defines a sonological model; several different psychoacoustical and analytical tools have been employed to this aim in the past. In this paper we will present a series of experiments conducted at the CSC‐University of Padova, which attempted to define an experimental framework for the development of algorithmically defined timbre spaces. Fundamental to our line of research has been the use of analysis methods borrowed from the speech processing community and of data‐representation techniques such as neural networks and statistical tools. The results show several analogies to the classical timbre spaces defined in the literature; this has proved very important in order to explore the qualities ...

/pdf/sonological-models-for-timbre-characterization-l4y49nqmox.pdf

Sonological models for timbre characterization

This paper describes an analysis of how the performance of a score differs when musicians are requested to play with differing expression. A professional violinist was asked to play short pieces of music in different versions expressing light, heavy, soft, hard, bright, and dark. For comparison, a normal, standard performance was recorded. Note‐by‐note analysis allowed us to identify the variations of the main acoustic parameters as a consequence of varying the expressive intentions. It was possible to identify two distinct expressive sources. The first refers the musical structure of the period, its division into phrases and the continuous alternation of tension and relaxation points. The second depends on the expressive intentions that the musician wants to convey to the listeners.

Note‐by‐note analysis of the influence of expressive intentions and musical structure in violin performance*

A timbre classification system based on auditory processing and Kohonen self organizing neural networks is described. Preliminary results are given on a simple classification experiment involving 12 instruments in both clean and degraded conditions.

Giovanni De Poli

Papers

Musical Signal Processing

Representations of musical signals

Sonological models for timbre characterization

Note‐by‐note analysis of the influence of expressive intentions and musical structure in violin performance*

Auditory modelling and self‐organizing neural networks for timbre classification