Heuristic Approach to Detect Software Application Crashes

It is valuable for a provider of a software library to gain insight into crashes of an application that may be caused by or otherwise related to the software library. Since the provider does not have access to the application codebase, it is not possible for the provider to obtain direct signals that an application crash occurred, or whether a crash was related to a library from the provider. This disclosure describes the use of a temporary dirty file to detect crashes that may be related to the use of an embedded library in an application. The dirty file is written to disk when the embedded code (library code) is first accessed or when the application is first launched. Upon successful completion of execution of the embedded code, the dirty file is automatically deleted. At a subsequent launch of the application or execution of the embedded code, if the dirty file still exists, it is determined that a crash of the application likely occurred since no application exit signal was received which would otherwise cause deletion of the dirty file

BIO-MIMETIC SENSORY MAPPING WITH ATTENTION FOR AUDITORY SCENE ANALYSIS

The human auditory system performs complex auditory tasks such as having a conversation in a busy cafe or picking the melodic line of a particular instrument in an ensemble orchestra, with remarkable ease. The human auditory system also exhibits ability to effortlessly adapt to constantly changing conditions and novel stimulus. The human auditory system achieves these through complex neuronal processes. First the low dimensional signal representing the acoustic stimulus is mapped to a higher dimensional space through a series of feed-forward neuronal transformations; wherein the different auditory objects in the scene are discernible. These feed-forward processes are then further complemented by the top-down processes like attention, driven by the cognitive regions to modulate the feed-forward processes in a manner that shines the spotlight on the object of interest; the interlocutor in the example of a busy cafe or the instrument of interest in the ensemble orchestra. In this thesis we explore leveraging these mechanisms observed in the mammalian brain, within computational frameworks, for addressing various auditory scene analysis tasks such as speech activity detection, environmental sound classification and source separation. We develop bio-mimetic computational strategies to model the feed-forward sensory mapping processes as well as the corresponding complimentary top-down mechanisms capable of modulating the feed-forward processes during attention. In the first part of the thesis, we show using Gabor filters as approximation for the feed-forward processes, that retuning the feed-forward processes under top-down attentional feedback is extremely potent in enabling robust behaviour in detecting speech activity. We introduce the notion of memory to represent prior knowledge of the acoustic objects and show that memories of objects can be used to deploy the necessary top-down feedback. In the next part of the thesis, we expand the feed-forward processes using data driven distributed deep belief system consisting of multiple streams to capture the stimulus from different spectrotemporal resolutions, a feature observed in the the human auditory system. We show that such a distributed system with inherent redundancies, further complimented by top-down attentional mechanisms using distributed object memories, allows for robust classification of environmental sounds in mismatched conditions. In the last part of the thesis, we show that incorporating the ideas of distributed processing and attentional mechanisms using deep neural networks leads to state-of-the-art performance for even complex tasks such as source separation. Further we show that in such a distributed system, the sum of the parts is better than the individual parts and that this aspect can be used to generate real-time top-down feedback; which in turn can be used to adapt the network to novel conditions during inference. Overall, the results of the thesis show that leveraging theses biologically inspired mechanisms within computational frameworks leads to enhanced robustness and adaptability to novel conditions, traits of the human auditory system that we sought to emulate

BIO-MIMETIC SENSORY MAPPING WITH ATTENTION FOR AUDITORY SCENE ANALYSIS

The human auditory system performs complex auditory tasks such as having a conversation in a busy cafe or picking the melodic line of a particular instrument in an ensemble orchestra, with remarkable ease. The human auditory system also exhibits ability to effortlessly adapt to constantly changing conditions and novel stimulus. The human auditory system achieves these through complex neuronal processes. First the low dimensional signal representing the acoustic stimulus is mapped to a higher dimensional space through a series of feed-forward neuronal transformations; wherein the different auditory objects in the scene are discernible. These feed-forward processes are then further complemented by the top-down processes like attention, driven by the cognitive regions to modulate the feed-forward processes in a manner that shines the spotlight on the object of interest; the interlocutor in the example of a busy cafe or the instrument of interest in the ensemble orchestra. In this thesis we explore leveraging these mechanisms observed in the mammalian brain, within computational frameworks, for addressing various auditory scene analysis tasks such as speech activity detection, environmental sound classification and source separation. We develop bio-mimetic computational strategies to model the feed-forward sensory mapping processes as well as the corresponding complimentary top-down mechanisms capable of modulating the feed-forward processes during attention. In the first part of the thesis, we show using Gabor filters as approximation for the feed-forward processes, that retuning the feed-forward processes under top-down attentional feedback is extremely potent in enabling robust behaviour in detecting speech activity. We introduce the notion of memory to represent prior knowledge of the acoustic objects and show that memories of objects can be used to deploy the necessary top-down feedback. In the next part of the thesis, we expand the feed-forward processes using data driven distributed deep belief system consisting of multiple streams to capture the stimulus from different spectrotemporal resolutions, a feature observed in the the human auditory system. We show that such a distributed system with inherent redundancies, further complimented by top-down attentional mechanisms using distributed object memories, allows for robust classification of environmental sounds in mismatched conditions. In the last part of the thesis, we show that incorporating the ideas of distributed processing and attentional mechanisms using deep neural networks leads to state-of-the-art performance for even complex tasks such as source separation. Further we show that in such a distributed system, the sum of the parts is better than the individual parts and that this aspect can be used to generate real-time top-down feedback; which in turn can be used to adapt the network to novel conditions during inference. Overall, the results of the thesis show that leveraging theses biologically inspired mechanisms within computational frameworks leads to enhanced robustness and adaptability to novel conditions, traits of the human auditory system that we sought to emulate

Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

In this paper we use a Non-negative Matrix Factorization/n(NMF) based approach to analyze the strokes of the mri-/ndangam, a South Indian hand drum, in terms of the normal/nmodes of the instrument. Using NMF, a dictionary of spectral/nbasis vectors are first created for each of the modes of the/nmridangam. The composition of the strokes are then studied/nby projecting them along the direction of the modes using/nNMF. We then extend this knowledge of each stroke in terms/nof its basic modes to transcribe audio recordings. Hidden/nMarkov Models are adopted to learn the modal activations for/neach of the strokes of the mridangam, yielding up to/n88,40%/naccuracy during transcription.This research/nwas partly funded by the European Research Council under the/nEuropean/nUnions Seventh Framework Program, as part of the CompMusic project/n(ERC grant agreement 267583)

Motivic analysis and its relevance to raga identification in carnatic music

A raga is a collective melodic expression consisting of motifs. A raga can be identified using motifs which are/nunique to it. Motifs can be thought of as signature prosodic phrases. Different ragas may be composed of the same set/nof notes, or even phrases, but the prosody may be completely different. In this paper, an attempt is made to determine/nthe characteristic motifs that enable identification of a raga and distinguish between them. To determine this, motifs are first manually marked for a set of five popular raga by a professional musician. The motifs are then normalised/nwith respect to the tonic. HMMs are trained for each motif using 80% of the data and about 20% are used for testing. The results do indicate that about 80% of the motifs are identified as belonging to a specific raga accurately.This research was partly funded by the European Research Council under the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant/nagreement 267583)

Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

In this paper we use a Non-negative Matrix Factorization/n(NMF) based approach to analyze the strokes of the mri-/ndangam, a South Indian hand drum, in terms of the normal/nmodes of the instrument. Using NMF, a dictionary of spectral/nbasis vectors are first created for each of the modes of the/nmridangam. The composition of the strokes are then studied/nby projecting them along the direction of the modes using/nNMF. We then extend this knowledge of each stroke in terms/nof its basic modes to transcribe audio recordings. Hidden/nMarkov Models are adopted to learn the modal activations for/neach of the strokes of the mridangam, yielding up to/n88,40%/naccuracy during transcription.This research/nwas partly funded by the European Research Council under the/nEuropean/nUnions Seventh Framework Program, as part of the CompMusic project/n(ERC grant agreement 267583)

Applause identification and its relevance to archival of Carnatic music

Comunicació presentada al 2nd CompMusic Workshop, celebrat els dies 12 i 13 de juliol de 2012 a Istanbul (Turquia), organitzat per CompMusic.A Carnatic music concert is made up of a sequence of pieces, where each piece corresponds to a particular genre and ra¯aga (melody). Unlike a western music concert, the artist may be applauded intra-performance /ninter-performance. Most Carnatic music that is archived today correspond to a single audio recordings of entire concerts./nThe purpose of this paper is to segment single audio recordings into a sequence of pieces using the/ncharacteristic features of applause and music. Spectral flux, spectral entropy change quite significantly from music to applause and vice-versa. The characteristics of these features for a subset of concerts was studied. A threshold based approach was used to segment the pieces into music fragments and applauses. Preliminary results/non recordings 19 concerts from matched microphones show that the EER is about 17% for a resolution of 0.25 seconds. Further, a parameter called CUSUM is estimated/nfor the applause regions. The CUSUM values determine the strength of the applause. The CUSUM is used to characterise the highlights of a concert.This research was partly funded by the European Research Council under the European Unions Seventh Framework Program, as part of the CompMusic project (ERC grant/nagreement 267583)

A knowledge based signal processing approach to tonic identification in indian classical music

In this paper, we describe several techniques for detecting tonic pitch value in Indian classical music. In Indian music, the raga is the basic melodic framework and it is built on the tonic. Tonic detection is therefore fundamental for any melodic analysis in Indian classical music. This work/nexplores detection of tonic by processing the pitch histograms of Indian classic music. Processing of pitch histograms using group delay functions and its ability to amplify certain traits of Indian music in the pitch histogram, is discussed. Three different strategies to detect tonic, namely, the concert method, the template matching and segmented histogram method are proposed. The concert method exploits the fact that the tonic is constant over a piece/concert./ntemplatematchingmethod and segmented histogrammethods/nuse the properties: (i) the tonic is always present in the background, (ii) some notes are less inflected and dominant, to detect the tonic of individual pieces. All the three methods yield good results for Carnatic music (90−100% accuracy), while for Hindustanimusic, the templatemethod works best, provided the v¯adi samv¯adi notes for a given piece are known (85%).This research was partly funded by the European Research Council/nunder the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant agreement/n267583

Motif spotting in an alapana in Carnatic music

ABSTRACT This work addresses the problem of melodic motif spotting, given a query, in Carnatic music. Melody in Carnatic music is based on the concept of raga. Melodic motifs are signature phrases which give a raga its identity. They are also the fundamental units that enable extempore elaborations of a raga. In this paper, an attempt is made to spot typical melodic motifs of a raga queried in a musical piece using a two pass dynamic programming approach, with pitch as the basic feature. In the first pass, the rough longest common subsequence (RLCS) matching is performed between the saddle points of the pitch contours of the reference motif and the musical piece. These saddle points corresponding to quasi-stationary points of the motifs, are relevant entities of the raga. Multiple sequences are identified in this step, not all of which correspond to the the motif that is queried. To reduce the false alarms, in the second pass a fine search using RLCS is performed between the continuous pitch contours of the reference motif and the subsequences obtained in the first pass. The proposed methodology is validated by testing on Alapanas of 20 different musicians

Applause identification and its relevance to archival of Carnatic music

Comunicació presentada al 2nd CompMusic Workshop, celebrat els dies 12 i 13 de juliol de 2012 a Istanbul (Turquia), organitzat per CompMusic.A Carnatic music concert is made up of a sequence of pieces, where each piece corresponds to a particular genre and ra¯aga (melody). Unlike a western music concert, the artist may be applauded intra-performance /ninter-performance. Most Carnatic music that is archived today correspond to a single audio recordings of entire concerts./nThe purpose of this paper is to segment single audio recordings into a sequence of pieces using the/ncharacteristic features of applause and music. Spectral flux, spectral entropy change quite significantly from music to applause and vice-versa. The characteristics of these features for a subset of concerts was studied. A threshold based approach was used to segment the pieces into music fragments and applauses. Preliminary results/non recordings 19 concerts from matched microphones show that the EER is about 17% for a resolution of 0.25 seconds. Further, a parameter called CUSUM is estimated/nfor the applause regions. The CUSUM values determine the strength of the applause. The CUSUM is used to characterise the highlights of a concert.This research was partly funded by the European Research Council under the European Unions Seventh Framework Program, as part of the CompMusic project (ERC grant/nagreement 267583)