Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods

ISOLATED INSTRUMENT TRANSCRIPTION USING A DEEP BELIEF NETWORK

ABSTRACT Automatic music transcription is a difficult task that has provoked extensive research on transcription systems that are predominantly general purpose, processing any number or type of instruments sounding simultaneously. This paper presents a polyphonic transcription system that is constrained to processing the output of a single instrument with an upper bound on polyphony. For example, a guitar has six strings and is limited to producing six notes simultaneously. The transcription system consists of a novel pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each audio analysis frame, such that the polyphony does not exceed that of the instrument. The implemented transcription system is evaluated on a compiled dataset of synthesized guitar recordings. Comparing these results to a prior single-instrument polyphonic transcription system that received exceptional results, this paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription

A Hardware Model Validation Tool for Use in Complex Space Systems

One of the many technological hurdles that must be overcome in future missions is the challenge of validating as-built systems against the models used for design. We propose a technique composed of intelligent parameter exploration in concert with automated failure analysis as a scalable method for the validation of complex space systems. The technique is impervious to discontinuities and linear dependencies in the data, and can handle dimensionalities consisting of hundreds of variables over tens of thousands of experiments

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results

Automatic guitar tablature transcription online

Manually transcribing guitar tablature from an audio recording is a difficult and time-consuming process, even for experienced guitarists. While several algorithms have been developed to automatically extract the notes occurring in an audio recording, and several algorithms have been developed to produce guitar tablature arrangements of notes occurring in a music score, no frameworks have been developed to facilitate the combination of these algorithms. This work presents a web-based guitar tablature transcription framework capable of generating guitar tablature arrangements directly from an audio recording. The implemented transcription framework, entitled Robotaba, facilitates the creation of web applications in which polyphonic transcription and guitar tablature arrangement algorithms can be embedded. Such a web application is implemented, resulting in a unified system that is capable of transcribing guitar tablature from a digital audio recording and displaying the resulting tablature in the web browser. The performance of the implemented polyphonic transcription and guitar tablature arrangement algorithms are evaluated using several metrics on a new dataset of manual transcriptions gathered from tablature websites.Transcrire à la main une tablature pour guitare à partir d'un enregistrement audio est un processus difficile et long, même pour les guitaristes chevronnés. Bien que plusieurs algorithmes aient été créés pour extraire automatiquement les notes d'un enregistrement audio, et d'autres pour préparer des arrangements de notes de tablature pour guitare tels qu'on les retrouve dans la création musicale, aucun environnement n'a été mise en place pour faciliter l'association de ces algorithmes. Le travail qui suit présente un environnement accessible sur l'Internet, permettant la transcription et la préparation d'arrangements de tablatures de guitare, directement à partir d'un enregistrement audio. Cet environnement de transcription, nommée Robotaba, facilite la création d'applications Web, dans lesquelles la transcription polyphonique et les algorithmes d'arrangements de tablature pour guitare peuvent être intégrés. Une telle application Web permet d'obtenir un système unifié, capable de transcrire une tablature pour guitare à partir d'un enregistrement audio numérique, et d'afficher la tablature obtenue dans un navigateur Web. La performance de la transcription polyphonique mise en place et des algorithmes d'arrangements de tablature pour guitare est évaluée à l'aide de plusieurs paramètres et d'un nouvel ensemble de données, constitué de transcriptions manuelles recueillies dans des sites Web consacrés aux tablatures

Isolated guitar transcription using a deep belief network

Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music. Manually transcribing audio recordings is a difficult and time-consuming process, even for experienced musicians. In response, several algorithms have been proposed to automatically analyze and transcribe the notes sounding in an audio recording; however, these algorithms are often general-purpose, attempting to process any number of instruments producing any number of notes sounding simultaneously. This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar. The transcription system consists of a novel note pitch estimation algorithm that uses a deep belief network and multi-label learning techniques to generate multiple pitch estimates for each analysis frame of the input audio signal. Using a compiled dataset of synthesized guitar recordings for evaluation, the algorithm described in this work results in an 11% increase in the f-measure of note transcriptions relative to Zhou et al.’s (2009) transcription algorithm in the literature. This paper demonstrates the effectiveness of deep, multi-label learning for the task of polyphonic transcription