294 research outputs found
The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems
This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates
Using a low-bit rate speech enhancement variable post-filter as a speech recognition system pre-filter to improve robustness to GSM speech
Includes bibliographical references.Performance of speech recognition systems degrades when they are used to recognize speech that has been transmitted through GS1 (Global System for Mobile Communications) voice communication channels (GSM speech). This degradation is mainly due to GSM speech coding and GSM channel noise on speech signals transmitted through the network. This poor recognition of GSM channel speech limits the use of speech recognition applications over GSM networks. If speech recognition technology is to be used unlimitedly over GSM networks recognition accuracy of GSM channel speech has to be improved. Different channel normalization techniques have been developed in an attempt to improve recognition accuracy of voice channel modified speech in general (not specifically for GSM channel speech). These techniques can be classified into three broad categories, namely, model modification, signal pre-processing and feature processing techniques. In this work, as a contribution toward improving the robustness of speech recognition systems to GSM speech, the use of a low-bit speech enhancement post-filter as a speech recognition system pre-filter is proposed. This filter is to be used in recognition systems in combination with channel normalization techniques
Speech assessment and characterization for law enforcement applications
Speech signals acquired, transmitted or stored in non-ideal conditions are often degraded by
one or more effects including, for example, additive noise. These degradations alter the signal
properties in a manner that deteriorates the intelligibility or quality of the speech signal. In
the law enforcement context such degradations are commonplace due to the limitations in
the audio collection methodology, which is often required to be covert. In severe degradation
conditions, the acquired signal may become unintelligible, losing its value in an investigation
and in less severe conditions, a loss in signal quality may be encountered, which can lead to
higher transcription time and cost.
This thesis proposes a non-intrusive speech assessment framework from which algorithms for
speech quality and intelligibility assessment are derived, to guide the collection and transcription
of law enforcement audio. These methods are trained on a large database labelled using
intrusive techniques (whose performance is verified with subjective scores) and shown to perform
favorably when compared with existing non-intrusive techniques. Additionally, a non-intrusive
CODEC identification and verification algorithm is developed which can identify a CODEC with
an accuracy of 96.8 % and detect the presence of a CODEC with an accuracy higher than 97 %
in the presence of additive noise.
Finally, the speech description taxonomy framework is developed, with the aim of characterizing
various aspects of a degraded speech signal, including the mechanism that results in a signal
with particular characteristics, the vocabulary that can be used to describe those degradations
and the measurable signal properties that can characterize the degradations. The taxonomy is
implemented as a relational database that facilitates the modeling of the relationships between
various attributes of a signal and promises to be a useful tool for training and guiding audio
analysts
Automatic Transcription of Bass Guitar Tracks applied for Music Genre Classification and Sound Synthesis
Musiksignale bestehen in der Regel aus einer Überlagerung mehrerer
Einzelinstrumente. Die meisten existierenden Algorithmen zur automatischen
Transkription und Analyse von Musikaufnahmen im Forschungsfeld des Music
Information Retrieval (MIR) versuchen, semantische Information direkt aus
diesen gemischten Signalen zu extrahieren. In den letzten Jahren wurde
häufig beobachtet, dass die Leistungsfähigkeit dieser Algorithmen durch
die Signalüberlagerungen und den daraus resultierenden Informationsverlust
generell limitiert ist. Ein möglicher Lösungsansatz besteht darin,
mittels Verfahren der Quellentrennung die beteiligten Instrumente vor der
Analyse klanglich zu isolieren. Die Leistungsfähigkeit dieser Algorithmen
ist zum aktuellen Stand der Technik jedoch nicht immer ausreichend, um eine
sehr gute Trennung der Einzelquellen zu ermöglichen. In dieser Arbeit
werden daher ausschließlich isolierte Instrumentalaufnahmen untersucht,
die klanglich nicht von anderen Instrumenten überlagert sind. Exemplarisch
werden anhand der elektrischen Bassgitarre auf die Klangerzeugung dieses
Instrumentes hin spezialisierte Analyse- und Klangsynthesealgorithmen
entwickelt und evaluiert.Im ersten Teil der vorliegenden Arbeit wird ein
Algorithmus vorgestellt, der eine automatische Transkription von
Bassgitarrenaufnahmen durchführt. Dabei wird das Audiosignal durch
verschiedene Klangereignisse beschrieben, welche den gespielten Noten auf
dem Instrument entsprechen. Neben den üblichen Notenparametern Anfang,
Dauer, Lautstärke und Tonhöhe werden dabei auch instrumentenspezifische
Parameter wie die verwendeten Spieltechniken sowie die Saiten- und Bundlage
auf dem Instrument automatisch extrahiert. Evaluationsexperimente anhand
zweier neu erstellter Audiodatensätze belegen, dass der vorgestellte
Transkriptionsalgorithmus auf einem Datensatz von realistischen
Bassgitarrenaufnahmen eine höhere Erkennungsgenauigkeit erreichen kann als
drei existierende Algorithmen aus dem Stand der Technik. Die Schätzung der
instrumentenspezifischen Parameter kann insbesondere für isolierte
Einzelnoten mit einer hohen Güte durchgeführt werden.Im zweiten Teil der
Arbeit wird untersucht, wie aus einer Notendarstellung typischer sich
wieder- holender Basslinien auf das Musikgenre geschlossen werden kann.
Dabei werden Audiomerkmale extrahiert, welche verschiedene tonale,
rhythmische, und strukturelle Eigenschaften von Basslinien quantitativ
beschreiben. Mit Hilfe eines neu erstellten Datensatzes von 520 typischen
Basslinien aus 13 verschiedenen Musikgenres wurden drei verschiedene
Ansätze für die automatische Genreklassifikation verglichen. Dabei zeigte
sich, dass mit Hilfe eines regelbasierten Klassifikationsverfahrens nur
Anhand der Analyse der Basslinie eines Musikstückes bereits eine mittlere
Erkennungsrate von 64,8 % erreicht werden konnte.Die Re-synthese der
originalen Bassspuren basierend auf den extrahierten Notenparametern wird
im dritten Teil der Arbeit untersucht. Dabei wird ein neuer
Audiosynthesealgorithmus vorgestellt, der basierend auf dem Prinzip des
Physical Modeling verschiedene Aspekte der für die Bassgitarre
charakteristische Klangerzeugung wie Saitenanregung, Dämpfung, Kollision
zwischen Saite und Bund sowie dem Tonabnehmerverhalten nachbildet.
Weiterhin wird ein parametrischerAudiokodierungsansatz diskutiert, der es
erlaubt, Bassgitarrenspuren nur anhand der ermittel- ten notenweisen
Parameter zu übertragen um sie auf Dekoderseite wieder zu
resynthetisieren. Die Ergebnisse mehrerer Hötest belegen, dass der
vorgeschlagene Synthesealgorithmus eine Re- Synthese von
Bassgitarrenaufnahmen mit einer besseren Klangqualität ermöglicht als die
Übertragung der Audiodaten mit existierenden Audiokodierungsverfahren, die
auf sehr geringe Bitraten ein gestellt sind.Music recordings most often consist of multiple instrument signals, which
overlap in time and frequency. In the field of Music Information Retrieval
(MIR), existing algorithms for the automatic transcription and analysis of
music recordings aim to extract semantic information from mixed audio
signals. In the last years, it was frequently observed that the algorithm
performance is limited due to the signal interference and the resulting
loss of information. One common approach to solve this problem is to first
apply source separation algorithms to isolate the present musical
instrument signals before analyzing them individually. The performance of
source separation algorithms strongly depends on the number of instruments
as well as on the amount of spectral overlap.In this thesis, isolated
instrumental tracks are analyzed in order to circumvent the challenges of
source separation. Instead, the focus is on the development of
instrument-centered signal processing algorithms for music transcription,
musical analysis, as well as sound synthesis. The electric bass guitar is
chosen as an example instrument. Its sound production principles are
closely investigated and considered in the algorithmic design.In the first
part of this thesis, an automatic music transcription algorithm for
electric bass guitar recordings will be presented. The audio signal is
interpreted as a sequence of sound events, which are described by various
parameters. In addition to the conventionally used score-level parameters
note onset, duration, loudness, and pitch, instrument-specific parameters
such as the applied instrument playing techniques and the geometric
position on the instrument fretboard will be extracted. Different
evaluation experiments confirmed that the proposed transcription algorithm
outperformed three state-of-the-art bass transcription algorithms for the
transcription of realistic bass guitar recordings. The estimation of the
instrument-level parameters works with high accuracy, in particular for
isolated note samples.In the second part of the thesis, it will be
investigated, whether the sole analysis of the bassline of a music piece
allows to automatically classify its music genre. Different score-based
audio features will be proposed that allow to quantify tonal, rhythmic, and
structural properties of basslines. Based on a novel data set of 520
bassline transcriptions from 13 different music genres, three approaches
for music genre classification were compared. A rule-based classification
system could achieve a mean class accuracy of 64.8 % by only taking
features into account that were extracted from the bassline of a music
piece.The re-synthesis of a bass guitar recordings using the previously
extracted note parameters will be studied in the third part of this thesis.
Based on the physical modeling of string instruments, a novel sound
synthesis algorithm tailored to the electric bass guitar will be presented.
The algorithm mimics different aspects of the instrument’s sound
production mechanism such as string excitement, string damping, string-fret
collision, and the influence of the electro-magnetic pickup. Furthermore, a
parametric audio coding approach will be discussed that allows to encode
and transmit bass guitar tracks with a significantly smaller bit rate than
conventional audio coding algorithms do. The results of different listening
tests confirmed that a higher perceptual quality can be achieved if the
original bass guitar recordings are encoded and re-synthesized using the
proposed parametric audio codec instead of being encoded using conventional
audio codecs at very low bit rate settings
Machine Learning for Auditory Hierarchy
Coleman, W. (2021). Machine Learning for Auditory Hierarchy. This dissertation is submitted for the degree of Doctor of Philosophy, Technological University Dublin. Audio content is predominantly delivered in a stereo audio file of a static, pre-formed mix. The content creator makes volume, position and effects decisions, generally for presentation in stereo speakers, but has no control ultimately over how the content will be consumed. This leads to poor listener experience when, for example, a feature film is mixed such that the dialogue is at a low level relative to the sound effects. Consumers can complain that they must turn the volume up to hear the words, but back down again because the effects levels are too loud. Addressing this problem requires a television mix optimised for the stereo speakers used in the vast majority of homes, which is not always available
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