Patrick J. Donnelly

Perceptual fusion of polyphonic pitch in cochlear implant users

In music, multiple pitches often occur simultaneously, an essential feature of harmony. In the present study, the authors assessed the ability of cochlear implant (CI) users to perceive polyphonic pitch. Acoustically presented stimuli consisted of one, two, or three superposed tones with different fundamental frequencies (f(0)). The normal hearing control group obtained significantly higher mean scores than the CI group. CI users performed near chance levels in recognizing two- and three-pitch stimuli, and demonstrated perceptual fusion of multiple pitches as single-pitch units. These results suggest that limitations in polyphonic pitch perception may significantly impair music perception in CI users.

Evolving four-part harmony using genetic algorithms

This paper presents a genetic algorithm that evolves a fourpart musical composition-melodically, harmonically, and rhythmically. Unlike similar attempts in the literature, our composition evolves from a single musical chord without human intervention or initial musical material. The mutation rules and fitness evaluation are based on common rules from music theory. The genetic operators and individual mutation rules are selected from probability distributions that evolve alongside the musical material.

Preservation of Rhythmic Clocking in Cochlear Implant Users: A Study of Isochronous Versus Anisochronous Beat Detection

The capacity for internal rhythmic clocking involves a relationship between perceived auditory input and subsequent cognitive processing by which isochronous auditory stimuli induce a temporal beat expectancy in a listener. Although rhythm perception has previously been examined in cochlear implant (CI) users through various tasks based primarily on rhythm pattern identification, such tasks may not have been sufficiently nuanced to detect defects in internal rhythmic clocking, which requires temporal integration on a scale of milliseconds. The present study investigated the preservation of such rhythmic clocking in CI participants through a task requiring detection of isochronicity in the final beat of a four-beat series presented at different tempos. Our results show that CI users performed comparably to normal hearing (NH) participants in all isochronous rhythm detection tasks but that professionally trained musicians (MUS) significantly outperformed both NH and CI participants. These results suggest that CI users have intact rhythm perception even on a temporally demanding task that requires tight preservation of timing differences between a series of auditory events. Also, these results suggest that musical training might improve rhythmic clocking in CI users beyond normal hearing levels, which may be useful in light of the deficits in spectral processing commonly observed in CI users.

Demonstrating semantic interoperability of diagnostic reasoners via AI-ESTATE

The Institute for Electrical and Electronics Engineers (IEEE), through its Standards Coordinating Committee 20 (SCC20), is developing interface standards focusing on Automatic Test System-related elements in cooperation with a Department of Defense (DoD) initiative to define, demonstrate, and recommend such standards.12 One of these standards-IEEE Std 1232–2002 Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-has been chosen for demonstration. Previously, we presented the results of the first phase of the AI-ESTATE demonstration, focusing on semantic interoperability of diagnostic models. The results of that demonstration successfully showed the effectiveness of semantic modeling in information exchange. In addition, the engineering burden imposed by stronger semantic requirements was demonstrated to be manageable. In the second phase, the focus was on supporting reasoner interoperability by implementing semantically defined software services in a service-oriented architecture. Here, we present an overview of the semantic interoperability problem in the context of diagnostic reasoning and discuss the results of the second phase of the demonstration.

Classification of musical timbre using bayesian networks

In this article, we explore the use of Bayesian networks for identifying the timbre of musical instruments. Peak spectral amplitude in ten frequency windows is extracted for each of 20 time windows to be used as features. Over a large data set of 24,000 audio examples covering the full musical range of 24 different common orchestral instruments, four different Bayesian network structures, including naive Bayes, are examined and compared with two support vector machines and a k-nearest neighbor classifier. Classification accuracy is examined by instrument, instrument family, and data set size. Bayesian networks with conditional dependencies in the time and frequency dimensions achieved 98 percent accuracy in the instrument classification task and 97 percent accuracy in the instrument family identification task. These results demonstrate a significant improvement over the previous approaches in the literature on this data set. Additionally, we tested our Bayesian approach on the widely used Iowa musical instrument data set, with similar results.

A standards-based approach to gray-scale health assessment using fuzzy fault trees

As part of a project to examine how current standards focused on test and diagnosis might be extended to address requirements for prognostics and health management, we have been exploring alternatives for incorporating facilities to represent gray-scale health information in the IEEE Std 1232 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). In this work, we extend the AI-ESTATE Common Element Model to provide “soft outcomes” on tests and diagnoses. We then demonstrate how to use these soft outcomes with the AI-ESTATE Fault Tree Model to implement a “fuzzy” fault tree. The resulting model then enables isolating faults within a system such that levels of degradation can also be tracked. In this paper, we describe the proposed extensions to AI-ESTATE as well as how those extensions work to implement a fuzzy fault tree using the demonstration circuit from previous Automatic Test Markup Language (ATML) demonstrations.

Implementing AI-ESTATE with prognostic extensions in Java

This paper is part of an ongoing effort to facilitate wider acceptance and further development of the IEEE Std 1232-2010 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). To that end, we describe a tool named SAPPHIRETM, which includes an implementation of AI-ESTATE in Java and a corresponding GUI tool that supports model creation and diagnostic inference of the standards Bayes Network Model (BNM). In addition, we describe extensions to the BNM as well as additional reasoner services that allow for representation and inference over dynamic Bayesian networks (DBNs) for standards-based prognostics.

Standard Diagnostic Services for the ATS framework

The US Navy has been supporting the demonstration of several IEEE standards with the intent of implementing these standards for future automatic test system procurement. In this paper, we discuss the second phase of a demonstration focusing on the IEEE P1232 AI-ESTATE standard. This standard specifies exchange formats and service interfaces for diagnostic reasoners. The first phase successfully demonstrated the ability to exchange diagnostic models through semantically enriched XML files. The second phase is focusing on the services and has been implemented using a web-based, service-oriented architecture. Here, we discuss implementation issues and preliminary results.

Cross-Dataset Validation of Feature Sets in Musical Instrument Classification

Automatically identifying the musical instruments present in audio recordings is a complex and difficult task. Although the focus has recently shifted to identifying instruments in a polyphonic setting, the task of identifying solo instruments has not been solved. Most empirical studies recognizing musical instruments use only a single dataset in the experiments, despiteevidence that mapproaches do not generalize from one dataset to another dataset. In this work, we present a method for data driven learning of spectral filters for use in feature extraction from audio recordings of solo musical instruments and discuss the extensibility of this approach to polyphonic mixtures of instruments. We examine four datasets of musical instrument sounds that have 13 instruments in common. We demonstrate cross-dataset validation by showing that a feature extraction scheme learned from one dataset can be used successfully for feature extraction and classification on another dataset.