Music classification by low-rank semantic mappings

A challenging open question in music classification is which music representation (i.e., audio features) and which machine learning algorithm is appropriate for a specific music classification task. To address this challenge, given a number of audio feature vectors for each training music recording that capture the different aspects of music (i.e., timbre, harmony, etc.), the goal is to find a set of linear mappings from several feature spaces to the semantic space spanned by the class indicator vectors. These mappings should reveal the common latent variables, which characterize a given set of classes and simultaneously define a multi-class linear classifier that classifies the extracted latent common features. Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms. Since the nuclear norm imposes rank constraints to the learnt mappings, the proposed method is referred to as low-rank semantic mappings (LRSMs). The performance of the LRSMs in music genre, mood, and multi-label classification is assessed by conducting extensive experiments on seven manually annotated benchmark datasets. The reported experimental results demonstrate the superiority of the LRSMs over the classifiers that are compared to. Furthermore, the best reported classification results are comparable with or slightly superior to those obtained by the state-of-the-art task-specific music classification methods

Music classification by low-rank semantic mappings

A challenging open question in music classification is which music representation (i.e., audio features) and which machine learning algorithm is appropriate for a specific music classification task. To address this challenge, given a number of audio feature vectors for each training music recording that capture the different aspects of music (i.e., timbre, harmony, etc.), the goal is to find a set of linear mappings from several feature spaces to the semantic space spanned by the class indicator vectors. These mappings should reveal the common latent variables, which characterize a given set of classes and simultaneously define a multi-class linear classifier that classifies the extracted latent common features. Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms. Since the nuclear norm imposes rank constraints to the learnt mappings, the proposed method is referred to as low-rank semantic mappings (LRSMs). The performance of the LRSMs in music genre, mood, and multi-label classification is assessed by conducting extensive experiments on seven manually annotated benchmark datasets. The reported experimental results demonstrate the superiority of the LRSMs over the classifiers that are compared to. Furthermore, the best reported classification results are comparable with or slightly superior to those obtained by the state-of-the-art task-specific music classification methods

Acoustic Features for Environmental Sound Analysis

International audienceMost of the time it is nearly impossible to differentiate between particular type of sound events from a waveform only. Therefore, frequency domain and time-frequency domain representations have been used for years providing representations of the sound signals that are more inline with the human perception. However, these representations are usually too generic and often fail to describe specific content that is present in a sound recording. A lot of work have been devoted to design features that could allow extracting such specific information leading to a wide variety of hand-crafted features. During the past years, owing to the increasing availability of medium scale and large scale sound datasets, an alternative approach to feature extraction has become popular, the so-called feature learning. Finally, processing the amount of data that is at hand nowadays can quickly become overwhelming. It is therefore of paramount importance to be able to reduce the size of the dataset in the feature space. The general processing chain to convert an sound signal to a feature vector that can be efficiently exploited by a classifier and the relation to features used for speech and music processing are described is this chapter

Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease

There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD

Artificial Bandwidth Extension of Speech Signals using Neural Networks

Although mobile wideband telephony has been standardized for over 15 years, many countries still do not have a nationwide network with good coverage. As a result, many cellphone calls are still downgraded to narrowband telephony. The resulting loss of quality can be reduced by artificial bandwidth extension. There has been great progress in bandwidth extension in recent years due to the use of neural networks. The topic of this thesis is the enhancement of artificial bandwidth extension using neural networks. A special focus is given to hands-free calls in a car, where the risk is high that the wideband connection is lost due to the fast movement. The bandwidth of narrowband transmission is not only reduced towards higher frequencies above 3.5 kHz but also towards lower frequencies below 300 Hz. There are already methods that estimate the low-frequency components quite well, which will therefore not be covered in this thesis. In most bandwidth extension algorithms, the narrowband signal is initially separated into a spectral envelope and an excitation signal. Both parts are then extended separately in order to finally combine both parts again. While the extension of the excitation can be implemented using simple methods without reducing the speech quality compared to wideband speech, the estimation of the spectral envelope for frequencies above 3.5 kHz is not yet solved satisfyingly. Current bandwidth extension algorithms are just able to reduce the quality loss due to narrowband transmission by a maximum of 50% in most evaluations. In this work, a modification for an existing method for excitation extension is proposed which achieves slight improvements while not generating additional computational complexity. In order to enhance the wideband envelope estimation with neural networks, two modifications of the training process are proposed. On the one hand, the loss function is extended with a discriminative part to address the different characteristics of phoneme classes. On the other hand, by using a GAN (generative adversarial network) for the training phase, a second network is added temporarily to evaluate the quality of the estimation. The neural networks that were trained are compared in subjective and objective evaluations. A final listening test addressed the scenario of a hands-free call in a car, which was simulated acoustically. The quality loss caused by the missing high frequency components could be reduced by 60% with the proposed approach.Obwohl die mobile Breitbandtelefonie bereits seit über 15 Jahren standardisiert ist, gibt es oftmals noch kein flächendeckendes Netz mit einer guten Abdeckung. Das führt dazu, dass weiterhin viele Mobilfunkgespräche auf Schmalbandtelefonie heruntergestuft werden. Der damit einhergehende Qualitätsverlust kann mit künstlicher Bandbreitenerweiterung reduziert werden. Das Thema dieser Arbeit sind Methoden zur weiteren Verbesserungen der Qualität des erweiterten Sprachsignals mithilfe neuronaler Netze. Ein besonderer Fokus liegt auf der Freisprech-Telefonie im Auto, da dabei das Risiko besonders hoch ist, dass durch die schnelle Fortbewegung die Breitbandverbindung verloren geht. Bei der Schmalbandübertragung fehlen neben den hochfrequenten Anteilen (etwa 3.5–7 kHz) auch tiefe Frequenzen unterhalb von etwa 300 Hz. Diese tieffrequenten Anteile können mit bereits vorhandenen Methoden gut geschätzt werden und sind somit nicht Teil dieser Arbeit. In vielen Algorithmen zur Bandbreitenerweiterung wird das Schmalbandsignal zu Beginn in eine spektrale Einhüllende und ein Anregungssignal aufgeteilt. Beide Anteile werden dann separat erweitert und schließlich wieder zusammengeführt. Während die Erweiterung der Anregung nahezu ohne Qualitätsverlust durch einfache Methoden umgesetzt werden kann ist die Schätzung der spektralen Einhüllenden für Frequenzen über 3.5 kHz noch nicht zufriedenstellend gelöst. Mit aktuellen Methoden können im besten Fall nur etwa 50% der durch Schmalbandübertragung reduzierten Qualität zurückgewonnen werden. Für die Anregungserweiterung wird in dieser Arbeit eine Variation vorgestellt, die leichte Verbesserungen erzielt ohne dabei einen Mehraufwand in der Berechnung zu erzeugen. Für die Schätzung der Einhüllenden des Breitbandsignals mithilfe neuronaler Netze werden zwei Änderungen am Trainingsprozess vorgeschlagen. Einerseits wird die Kostenfunktion um einen diskriminativen Anteil erweitert, der das Netz besser zwischen verschiedenen Phonemen unterscheiden lässt. Andererseits wird als Architektur ein GAN (Generative adversarial network) verwendet, wofür in der Trainingsphase ein zweites Netz verwendet wird, das die Qualität der Schätzung bewertet. Die trainierten neuronale Netze wurden in subjektiven und objektiven Tests verglichen. Ein abschließender Hörtest diente zur Evaluierung des Freisprechens im Auto, welches akustisch simuliert wurde. Der Qualitätsverlust durch Wegfallen der hohen Frequenzanteile konnte dabei mit dem vorgeschlagenen Ansatz um etwa 60% reduziert werden

Computation of the one-dimensional unwrapped phase

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 101-102). "Cepstrum bibliography" (p. 67-100).In this thesis, the computation of the unwrapped phase of the discrete-time Fourier transform (DTFT) of a one-dimensional finite-length signal is explored. The phase of the DTFT is not unique, and may contain integer multiple of 27r discontinuities. The unwrapped phase is the instance of the phase function chosen to ensure continuity. This thesis presents existing algorithms for computing the unwrapped phase, discussing their weaknesses and strengths. Then two composite algorithms are proposed that use the existing ones, combining their strengths while avoiding their weaknesses. The core of the proposed methods is based on recent advances in polynomial factoring. The proposed methods are implemented and compared to the existing ones.by Zahi Nadim Karam.S.M