1,964 research outputs found
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
Discrimination of age, sex, and individual identity using the upcall of the North Atlantic right whale (Eubalaena glacialis)
According to the source-filter theory proposed for human speech, physical attributes of the mammalian vocal production mechanism combine independently to result in individually distinctive vocalizations. In the case of stereotyped calls with all individuals producing a similar frequency contour, formants resulting from the shape and size of the vocal tract may be more likely to contain individually distinctive information than the fundamental frequency resulting from the vibrating source. However, the formant structure resulting from such filtering has been historically undervalued in the majority of studies addressing individual distinctiveness in non-human species. The upcall of the North Atlantic right whale (Eubalaena glacialis) is characterized as a stereotyped contact call, and visual inspection of upcall spectrograms confirms presence of a robust formant structure. Here I present results testing age, sex, and individual distinctiveness of upcalls recorded from archival, suction cup mounted tags (Dtags). Multiple measurements were made using the fundamental frequency contour, formant structure, and amplitude of the upcalls. These three variable groupings were then tested alone and in combination with other groupings to assign upcalls to age classes based on reproductive maturity, age classes based on size, sex, and individual whales. To compare multiple classification methods, I used both discriminant function analysis and a classification and regression tree to classify calls to appropriate groups. In all analyses, the percentage of calls correctly assigned to the correct group—age, sex, individual—was significantly higher than chance levels. These results represent the first quantitative analysis of individual distinctiveness in mysticete whales and provide a baseline for further development of acoustic detection techniques that could be used to noninvasively track movements of whales across multiple habitats
National Wind Tunnel Complex (NWTC)
The National Wind Tunnel Complex (NWTC) Final Report summarizes the work carried out by a unique Government/Industry partnership during the period of June 1994 through May 1996. The objective of this partnership was to plan, design, build and activate 'world class' wind tunnel facilities for the development of future-generation commercial and military aircraft. The basis of this effort was a set of performance goals defined by the National Facilities Study (NFS) Task Group on Aeronautical Research and Development Facilities which established two critical measures of improved wind tunnel performance; namely, higher Reynolds number capability and greater productivity. Initial activities focused upon two high-performance tunnels (low-speed and transonic). This effort was later descoped to a single multipurpose tunnel. Beginning in June 1994, the NWTC Project Office defined specific performance requirements, planned site evaluation activities, performed a series of technical/cost trade studies, and completed preliminary engineering to support a proposed conceptual design. Due to budget uncertainties within the Federal government, the NWTC project office was directed to conduct an orderly closure following the Systems Design Review in March 1996. This report provides a top-level status of the project at that time. Additional details of all work performed have been archived and are available for future reference
Models for learning reverberant environments
Reverberation is present in all real life enclosures. From our workplaces to our homes and even in places designed as auditoria, such as concert halls and theatres. We have learned to understand speech in the presence of reverberation and also to use it for aesthetics in music. This thesis investigates novel ways enabling machines to learn the properties of reverberant acoustic environments. Training machines to classify rooms based on the effect of reverberation requires the use of data recorded in the room. The typical data for such measurements is the Acoustic Impulse Response (AIR) between the speaker and the receiver as a Finite Impulse Response (FIR) filter. Its representation however is high-dimensional and the measurements are small in number, which limits the design and performance of deep learning algorithms. Understanding properties of the rooms relies on the analysis of reflections that compose the AIRs and the decay and absorption of the sound energy in the room. This thesis proposes novel methods for representing the early reflections, which are strong and sparse in nature and depend on the position of the source and the receiver. The resulting representation significantly reduces the coefficients needed to represent the AIR and can be combined with a stochastic model from the literature to also represent the late reflections. The use of Finite Impulse Response (FIR) for the task of classifying rooms is investigated, which provides novel results in this field. The aforementioned issues related to AIRs are highlighted through the analysis. This leads to the proposal of a data augmentation method for the training of the classifiers based on Generative Adversarial Networks (GANs), which uses existing data to create artificial AIRs, as if they were measured in real rooms. The networks learn properties of the room in the space defined by the parameters of the low-dimensional representation that is proposed in this thesis.Open Acces
Polyphonic music information retrieval based on multi-label cascade classification system
Recognition and separation of sounds played by various instruments is very useful in labeling audio files with semantic information. This is a non-trivial task requiring sound analysis, but the results can aid automatic indexing and browsing music data when searching for melodies played by user specified instruments. Melody match based on pitch detection technology has drawn much attention and a lot of MIR systems have been developed to fulfill this task. However, musical instrument recognition remains an unsolved problem in the domain. Numerous approaches on acoustic feature extraction have already been proposed for timbre recognition. Unfortunately, none of those monophonic timbre estimation algorithms can be successfully applied to polyphonic sounds, which are the more usual cases in the real music world. This has stimulated the research on multi-labeled instrument classification and new features development for content-based automatic music information retrieval. The original audio signals are the large volume of unstructured sequential values, which are not suitable for traditional data mining algorithms; while the acoustical features are sometime not sufficient for instrument recognition in polyphonic sounds because they are higher-level representatives of raw signal lacking details of original information. In order to capture the patterns which evolve on the time scale, new temporal features are introduced to supply more temporal information for the timbre recognition. We will introduce the multi-labeled classification system to estimate multiple timbre information from the polyphonic sound by classification based on acoustic features and short-term power spectrum matching. In order to achieve higher estimation rate, we introduced the hierarchically structured cascade classification system under the inspiration of the human perceptual process. This cascade classification system makes a first estimate on the higher level decision attribute, which stands for the musical instrument family. Then, the further estimation is done within that specific family range. Experiments showed better performance of a hierarchical system than the traditional flat classification method which directly estimates the instrument without higher level of family information analysis.
Traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for the cascade system. We introduce the new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show the higher accuracy of cascade system with the new schema compared to the traditional schemas. The query answering system is built based on the cascade classifier
Report of the Working Group on Marine Habitat Mapping (WGMHM) [5-8 April, Bremerhaven, Germany]
Contributor: Pål Mortense
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
Automated bioacoustics:methods in ecology and conservation and their potential for animal welfare monitoring
Vocalizations carry emotional, physiological and individual information. This suggests that they may serve as potentially useful indicators for inferring animal welfare. At the same time, automated methods for analysing and classifying sound have developed rapidly, particularly in the fields of ecology, conservation and sound scene classification. These methods are already used to automatically classify animal vocalizations, for example, in identifying animal species and estimating numbers of individuals. Despite this potential, they have not yet found widespread application in animal welfare monitoring. In this review, we first discuss current trends in sound analysis for ecology, conservation and sound classification. Following this, we detail the vocalizations produced by three of the most important farm livestock species: chickens (Gallus gallus domesticus), pigs (Sus scrofa domesticus) and cattle (Bos taurus). Finally, we describe how these methods can be applied to monitor animal welfare with new potential for developing automated methods for large-scale farming
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