Christensen, R. (2002), Plane Answers to Complex Questions: The Theory of Linear Models (3rd ed.), New York: Springer-Verlag. Crocker, D. C. (1980), Review of Linear Regression Analysis, by G. A. F. Seber, Technometrics, 22, 130. Datta, B. N. (1995), Numerical Linear Algebra and Applications, Paci c Grove, CA: Brooks/Cole. Draper, N. R. (2002), “Applied Regression Analysis Bibliography Update 2000–2001,” Communications in Statistics: Theory and Methods, 2051– 2075. Golub, G. H., and Van Loan, C. F. (1996), Matrix Computations (3rd ed.), Baltimore, MD: Johns Hopkins University Press. Graybill, F. A. (2000), Theory and Application of the Linear Model, Paci c Grove, CA: Brooks/Cole. Hocking, R. R. (2003), Methods and Applications of Linear Models: Regression and the Analysis of Variance (2nd ed.), New York: Wiley. Porat, B. (1993), Digital Processing of Random Signals, Englewood Cliffs, NJ: Prentice-Hall. Ravishanker, N., and Dey, D. K. (2002), A First Course in Linear Model Theory, Boca Raton, FL: Chapman and Hall/CRC. White, H. (1984), Asymptotic Theory for Econometricians, Orlando, FL: Academic Press.

Generalized Estimating Equations

The Journal of the Acoustical Society of America

Preface vii Acknowledgments xi Chapter 1. What Is Language? 1 Chapter 2. Do Animals Have Language? 21 Chapter 3. In the Beginning Was the Gesture 41 Chapter 4. On Our Own Two Feet 66 Chapter 5. Becoming Human 82 Chapter 6. Signed Language 102 Chapter 7. It's All Talk 126 Chapter 8. Why Are We Lopsided? 159 Chapter 9. From Hand to Mouth 184 Chapter 10. Synopsis 213 References 221 Index 247

Gestural Gab@@@From Hand to Mouth: The Origins of Language

https://engineering.jhu.edu/lcap/data/uploads/pdfs/tins2011_shamma.pdf

Temporal coherence and attention in auditory scene analysis

The title of this technical report says almost everything: this is indeed \"a short bibliography on AI and the arts\". It is presented in four sections: General Arguments, Proposals, and Approaches (31 references); Artificial Intelligence in Music (124 references); Artificial Intelligence in Literature and the Performing Arts (13 references), and Artificial Intelligence and Visual Art (57 references). About a quarter of these have short abstracts. Creating a bibliography can be a monumental task, and this bibliography should be viewed as a good and useful start, though it is by no means complete. For comparison, consider the 4,585-entry bibliography Computer Applications in Music by Deta Davis (A-REditions). No direct comparison is intended (or possible), but my point is that many more papers are likely to exist. As a rough check, I looked for several pre-1990 AI and Music articles and books (including my own, of course) in the bibliography. Out of five papers from well-known sources, only one was listed. On the other hand, I discovered a number of papers in this report that were unknown to me, so I am grateful to have a new source of references. In their introduction, the authors acknowledge the need for more references and even offer.a cup of coffee in reward for each new one. I will be sending a number of contributions, so the next time anyone is in Vienna, the coffee is on me. I hope the authors will continue to collect abstracts and publish an updated report in the future.

Auditory Scene Analysis: The Perceptual Organization of Sound by Albert Bregman (review)

The analysis of musical signals to extract audio descriptors that can potentially characterize their timbre has been disparate and often too focused on a particular small set of sounds. The Timbre Toolbox provides a comprehensive set of descriptors that can be useful in perceptual research, as well as in music information retrieval and machine-learning approaches to content-based retrieval in large sound databases. Sound events are first analyzed in terms of various input representations (short-term Fourier transform, harmonic sinusoidal components, an auditory model based on the equivalent rectangular bandwidth concept, the energy envelope). A large number of audio descriptors are then derived from each of these representations to capture temporal, spectral, spectrotemporal, and energetic properties of the sound events. Some descriptors are global, providing a single value for the whole sound event, whereas others are time-varying. Robust descriptive statistics are used to characterize the time-varying descriptors. To examine the information redundancy across audio descriptors, correlational analysis followed by hierarchical clustering is performed. This analysis suggests ten classes of relatively independent audio descriptors, showing that the Timbre Toolbox is a multidimensional instrument for the measurement of the acoustical structure of complex sound signals.

/pdf/the-timbre-toolbox-extracting-audio-descriptors-from-musical-5085osbbl0.pdf

The Timbre Toolbox: extracting audio descriptors from musical signals.

http://psychologie.univ-fcomte.fr/download/section-psychologie/document/pdf/susini-etal2004.pdf

Characterizing the sound quality of air-conditioning noise

It is well-established that subjective judgments of perceived urgency of alarm sounds can be affected by acoustic parameters. In this study, the authors investigated an objective measurement, the reaction time (RT), to test the effectiveness of temporal parameters of sounds in the context of warning sounds. Three experiments were performed using a RT paradigm, with two different concurrent visuomotor tracking tasks simulating driving conditions. Experiments 1 and 2 show that RT decreases as interonset interval (IOI) decreases, where IOI is defined as the time elapsed from the onset of one sound pulse to the onset of the next. Experiment 3 shows that temporal irregularity between pulses can capture a listener’s attention. These findings lead to concrete recommendations: IOI can be used to modulate warning sound urgency; and temporal irregularity can provoke an arousal effect in listeners. The authors also argue that the RT paradigm provides a useful tool for clarifying some of the factors involved in alarm processing.

/pdf/evaluating-warning-sound-urgency-with-reaction-times-3hwpnd1ttq.pdf

Evaluating Warning Sound Urgency With Reaction Times

The influence of listener's expertise and sound identification on the categorization of environmental sounds is reported in three studies. In Study 1, the causal uncertainty of 96 sounds was measured by counting the different causes described by 29 participants. In Study 2, 15 experts and 15 nonexperts classified a selection of 60 sounds and indicated the similarities they used. In Study 3, 38 participants indicated their confidence in identifying the sounds. Participants reported using either acoustical similarities or similarities of the causes of the sounds. Experts used acoustical similarity more often than nonexperts, who used the similarity of the cause of the sounds. Sounds with a low causal uncertainty were more often grouped together because of the similarities of the cause, whereas sounds with a high causal uncertainty were grouped together more often because of the acoustical similarities. The same conclusions were reached for identification confidence. This measure allowed the sound classification to be predicted, and is a straightforward method to determine the appropriate description of a sound.

/pdf/listener-expertise-and-sound-identification-influence-the-38qjh4956n.pdf

Listener expertise and sound identification influence the categorization of environmental sounds.

In this article we report on listener categorization of meaningful environmental sounds. A starting point for this study was the phenomenological taxonomy proposed by Gaver (1993b). In the first experimental study, 15 participants classified 60 environmental sounds and indicated the properties shared by the sounds in each class. In a second experimental study, 30 participants classified and described 56 sounds exclusively made by solid objects. The participants were required to concentrate on the actions causing the sounds independent of the sound source. The classifications were analyzed with a specific hierarchical cluster technique that accounted for possible cross-classifications, and the verbalizations were submitted to statistical lexical analyses. The results of the first study highlighted 4 main categories of sounds: solids, liquids, gases, and machines. The results of the second study indicated a distinction between discrete interactions (e.g., impacts) and continuous interactions (e.g., tearing) and suggested that actions and objects were not independent organizational principles. We propose a general structure of environmental sound categorization based on the sounds' temporal patterning, which has practical implications for the automatic classification of environmental sounds.

/pdf/a-lexical-analysis-of-environmental-sound-categories-269mec8j3s.pdf

Patrick Susini

Papers

The Timbre Toolbox: extracting audio descriptors from musical signals.

Characterizing the sound quality of air-conditioning noise

Evaluating Warning Sound Urgency With Reaction Times

Listener expertise and sound identification influence the categorization of environmental sounds.

A Lexical Analysis of Environmental Sound Categories.