This paper gives HRTF magnitude data in numerical form for 43 frequencies between 0.2---12 kHz, the average of 12 studies representing 100 different subjects. However, no phase data is included in the tables; group delay simulation would need to be included in order to account for ITD. In 3-D sound applications intended for many users, we want might want to use HRTFs that represent the common features of a number of individuals. But another approach might be to use the features of a person who has desirable HRTFs, based on some criteria. (One can sense a future 3-D sound system where the pinnae of various famous musicians are simulated.) A set of HRTFs from a good localizer (discussed in Chapter 2) could be used if the criterion were localization performance. If the localization ability of the person is relatively accurate or more accurate than average, it might be reasonable to use these HRTF measurements for other individuals. The Convolvotron 3-D audio system (Wenzel, Wightman, and Foster, 1988) has used such sets particularly because elevation accuracy is affected negatively when listening through a bad localizers ears (see Wenzel, et al., 1988). It is best when any single nonindividualized HRTF set is psychoacoustically validated using a 113 statistical sample of the intended user population, as shown in Chapter 2. Otherwise, the use of one HRTF set over another is a purely subjective judgment based on criteria other than localization performance. The technique used by Wightman and Kistler (1989a) exemplifies a laboratory-based HRTF measurement procedure where accuracy and replicability of results were deemed crucial. A comparison of their techniques with those described in Blauert (1983), Shaw (1974), Mehrgardt and Mellert (1977), Middlebrooks, Makous, and Gree...

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This paper argues for a special focus on the use of dynamic human interaction to explore datasets while they are being transformed into sound. We describe why this is a special case of both human computer interaction (HCI) techniques and sonification methods. Humans are adapted for interacting with their physical environment and making continuous use of all their senses. When this exploratory interaction is applied to a dataset (by continuously controlling its transformation into sound) new insights are gained into the data’s macro and micro-structure, which are not obvious in a visual rendering. This paper reviews the importance of interaction in sonification, describes how a certain quality of interaction is required, provides examples of the techniques being applied interactively, and outlines a plan of future work to develop interaction techniques to aid sonification. 1.

Sounds are often the result of motions of virtual objects in a virtual environment. Therefore, sounds and the motions that caused them should be treated in an integrated way. When sounds and motions do not have the proper correspondence, the resultant confusion can lessen the effects of each. In this paper, we present an integrated system for modeling, synchronizing, and rendering sounds for virtual environments. The key idea of the system is the use of a functional representation of sounds, called timbre trees. This representation is used to model sounds that are parameterizable. These parameters can then be mapped to the parameters associated with the motions of objects in the environment. This mapping allows the correspondence of motions and sounds in the environment. Representing arbitrary sounds using timbre trees is a difficult process that we do not address in this paper. We describe approaches for creating some timbre trees including the use of genetic algorithms. Rendering the...

Two systems are reviewed than perform automatic music transcription. The first perform monophonic transcription using an autocorrelation pitch tracker. The algorithm takes advantage of some heuristic parameters related to the similarity between image and sound in the collector. The detection is correct between notes B1 to E6 and further timbre analysis will provide the necessary parameters to reproduce a similar copy of the original sound. The second system is able to analyse simple polyphonic tracks. It is composed of a blackboard system, receiving its input from a segmentation routine in the form of an averaged STFT matrix. The blackboard contents an hypotheses database, an scheduler and knowledge sources, one of which is a neural network chord recogniser with the ability to reconfigure the operation of the system, allowing it to output more than one note hypothesis at the time. Some examples are provided to illustrate the performance and the weaknesses of the current implementation. Next steps for further development are defined.

Background in automatic music performance. Several representational levels are involved in the performance process [Dannenberg, 1993]: a performance environment should be concerned at least in i) getting a score in input, ii) analyzing it, like a human performer would do, iii) modelling the constraints posed by body-instrument interaction, iv) manipulating sound parameters. While there exist some satisfactory solutions at level iv) (e.g. the guitar physical model described in [Cuzzucoli, Lombardo, 1999]), levels ii) and iii) still pose unanswered questions. In this paper we address the problem of fingering, that deeply affects the physical and expressive qualities of the sound being produced (cf. [Parncutt et al. 1997] and [Gilardino, 1975a, 1975b] in the case of keyboards and guitar, respectively). Background in computing. Parncutt et al. (1997) propose an ergonomic model for short fragments of keyboard music. The algorithmic approach in [Sayegh, 1989] proposes a computationally efficient solution (based on a shortest path algorithm) to the fingering process in string instruments. Aims. We aim to show that cognitive-based and suitable fingerings for string instruments can be achieved by a computationally efficient algorithm. Main contribution. Our work might be regarded as a first step in extending Sayegh’s approach in a cognitive direction: while Sayegh computes the optimum (i.e. the least difficult one) fingering considering the whole piece, we take into account a number of features concerning the musical intentions, that underlie the fingering decision process. In this

This thesis describes an approach to representing musical rhythm in computational terms. The purpose of such an approach is to provide better models of musical time for machine accompaniment of human musicians and in that attempt, to better understand the processes behind human perception and performance. The intersections between musicology and artificial intelligence (AI) are reviewed, describing the rewards from the interdisciplinary study of music with AI techniques, and the converse benefits to AI research. The arguments for formalisation of musicological theories using AI and cognitive science concepts are presented. These bear upon the approach of research, considering ethnographic and process models of music versus traditionally descriptive methods of music study. This enquiry investigates the degree to which the human task of music can be studied and modelled computationally. It simultaneously performs the AI task of problem domain identification and constraint. The psycholo...

Abstract—The capacity for real-time synchronization and coordination is a common ability among trained musicians performing a music score that presents an interesting challenge for machine intelligence. Compared to speech recognition, which has influenced many music information retrieval systems, music’s temporal dynamics and complexity pose challenging problems to common approximations regarding time modeling of data streams. In this paper, we propose a design for a real-time music-to-score alignment system. Given a live recording of a musician playing a music score, the system is capable of following the musician in real time within the score and decoding the tempo (or pace) of its performance. The proposed design features two coupled audio and tempo agents within a unique probabilistic inference framework that adaptively updates its parameters based on the real-time context. Online decoding is achieved through the collaboration of the coupled agents in a Hidden Hybrid Markov/semi-Markov framework, where prediction feedback of one agent affects the behavior of the other. We perform evaluations for both real-time alignment and the proposed temporal model. An implementation of the presented system has been widely used in real concert situations worldwide and the readers are encouraged to access the actual system and experiment the results. Index Terms—Automatic musical accompaniment, hidden hybrid Markov/semi-Markov models, computer music. Ç 1

Digital sound synthesizers, ubiquitous today in sound cards, software and dedicated hardware, use algorithms (Sound Synthesis Techniques, SSTs) capable of generating sounds similar to those of acoustic instruments and even totally novel sounds. The design of SSTs is a very hard problem. It is usually assumed that it requires human ingenuity to design an algorithm suitable for synthesizing a sound with certain characteristics. Many of the SSTs commonly used are the fruit of experimentation and a long refinement processes. A SST is determined by its “functional form ” and “internal parameters”. Design of SSTs is usually done by selecting a fixed functional form from a handful of commonly used SSTs, and performing a parameter estimation technique to find a set of internal parameters that will best emulate the target sound. A new approach for automating the design of SSTs is proposed. It uses a set of examples of the desired behavior of the SST in the form of “inputs + target sound”. The approach is capable of suggesting novel functional forms and their internal parameters, suited to follow closely the given examples.

This paper is a very basic one, where one tries to explain how one can write a digital audio effect in a non-real time situation with a very general mathematical language such as MATLAB, and how such digital audio effects can be used in the real life. 1 What is a digital audio effect? The term digital audio effects has been used as an acronym for the COST action G6. So what is a digital audio effect? . audio-effect input output parameters curves First of all it is not a "sound effect", in the sense of a collection of sounds be them natural or artificial. A digital audio effect is a process which when applied to a sound modifies it in a way to improve it or at least make some aspect of it more evident. Some of these effects come from the analog electronics. Reverberation units for example were the good companion of mixing tables and equalizers. Then computers came and it was possible to control different units, or even do some digital processing in real-time or out of real time....