Comparison of input devices in an ISEE direct timbre manipulation task

The representation and manipulation of sound within multimedia systems is an important and currently under-researched area. The paper gives an overview of the authors' work on the direct manipulation of audio information, and describes a solution based upon the navigation of four-dimensional scaled timbre spaces. Three hardware input devices were experimentally evaluated for use in a timbre space navigation task: the Apple Standard Mouse, Gravis Advanced Mousestick II joystick (absolute and relative) and the Nintendo Power Glove. Results show that the usability of these devices significantly affected the efficacy of the system, and that conventional low-cost, low-dimensional devices provided better performance than the low-cost, multidimensional dataglove

Explicit Mapping of Acoustic Regimes For Wind Instruments

This paper proposes a methodology to map the various acoustic regimes of wind instruments. The maps can be generated in a multi-dimensional space consisting of design, control parameters, and initial conditions. The bound- aries of the maps are obtained explicitly in terms of the parameters using a support vector machine (SVM) classifier as well as a dedicated adaptive sam- pling scheme. The approach is demonstrated on a simplified clarinet model for which several maps are generated based on different criteria. Examples of computation of the probability of occurrence of a specific acoustic regime are also provided. In addition, the approach is demonstrated on a design optimization example for optimal intonation

Metamorph: Real-Time High-Level Sound Transformations Based On A Sinusoids Plus Noise Plus Transients Model

Spectral models provide ways to manipulate musical audio signals that can be both powerful and intuitive, but high-level control is often required in order to provide flexible real-time control over the potentially large parameter set. This paper introduces Metamorph, a new open source library for high-level sound transformation. We describe the real-time sinusoids plus noise plus transients model that is used by Metamorph and explain the opportunities that it provides for sound manipulation

The human 'pitch center' responds differently to iterated noise and Huggins pitch

A magnetoencephalographic marker for pitch analysis (the pitch onset response) has been reported for different types of pitch-evoking stimuli, irrespective of whether the acoustic cues for pitch are monaurally or binaurally produced. It is claimed that the pitch onset response reflects a common cortical representation for pitch, putatively in lateral Heschl's gyrus. The result of this functional MRI study sheds doubt on this assertion. We report a direct comparison between iterated ripple noise and Huggins pitch in which we reveal a different pattern of auditory cortical activation associated with each pitch stimulus, even when individual variability in structure-function relations is accounted for. Our results suggest it may be premature to assume that lateral Heschl's gyrus is a universal pitch center

Resonant Neural Dynamics of Speech Perception

What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent representations of syllables and words? What sorts of brain mechanisms encode the correct temporal order, despite such backwards effects, during speech perception? How does the brain extract rate-invariant properties of variable-rate speech? This article describes an emerging neural model that suggests answers to these questions, while quantitatively simulating challenging data about audition, speech and word recognition. This model includes bottom-up filtering, horizontal competitive, and top-down attentional interactions between a working memory for short-term storage of phonetic items and a list categorization network for grouping sequences of items. The conscious speech and word recognition code is suggested to be a resonant wave of activation across such a network, and a percept of silence is proposed to be a temporal discontinuity in the rate with which such a resonant wave evolves. Properties of these resonant waves can be traced to the brain mechanisms whereby auditory, speech, and language representations are learned in a stable way through time. Because resonances are proposed to control stable learning, the model is called an Adaptive Resonance Theory, or ART, model.Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-01-1-0624)

Response of an artificially blown clarinet to different blowing pressure profiles

Using an artificial mouth with an accurate pressure control, the onset of the pressure oscillations inside the mouthpiece of a simplified clarinet is studied experimentally. Two time profiles are used for the blowing pressure: in a first set of experiments the pressure is increased at constant rates, then decreased at the same rate. In a second set of experiments the pressure rises at a constant rate and is then kept constant for an arbitrary period of time. In both cases the experiments are repeated for different increase rates. Numerical simulations using a simplified clarinet model blown with a constantly increasing mouth pressure are compared to the oscillating pressure obtained inside the mouthpiece. Both show that the beginning of the oscillations appears at a higher pressure values than the theoretical static threshold pressure, a manifestation of bifurcation delay. Experiments performed using an interrupted increase in mouth pressure show that the beginning of the oscillation occurs close to the stop in the increase of the pressure. Experimental results also highlight that the speed of the onset transient of the sound is roughly the same, independently of the duration of the increase phase of the blowing pressure.Comment: 14 page

Audio Features Affected by Music Expressiveness

Within a Music Information Retrieval perspective, the goal of the study presented here is to investigate the impact on sound features of the musician's affective intention, namely when trying to intentionally convey emotional contents via expressiveness. A preliminary experiment has been performed involving $10$ tuba players. The recordings have been analysed by extracting a variety of features, which have been subsequently evaluated by combining both classic and machine learning statistical techniques. Results are reported and discussed.Comment: Submitted to ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), Pisa, Italy, July 17-21, 201