Data Mining Practical Machine Learning Tools and Techniques

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A morphable model for the synthesis of 3D faces

http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

A study of the automatic classification of musical instrument sounds is presented. For this purpose a database of musical instrument sound parameters was built which consists of musical instrument recordings and their parametric representations. The parameterization process was conceived and performed in order to find significant musical instrument sound features and to remove redundancy from the musical signal. Classification experiments of musical instrument sounds were performed with neural networks allowing a discussion of the efficiency of the feature extraction process and its limitations. Conclusions and remarks concerning further development of this study and its relation to the current MPEG-7 standardization process are included.

https://sound.eti.pg.gda.pl/papers/Representing_musical_instrument.pdf

Representing Musical Instrument Sounds for Their Automatic Classification

A review of available audio-visual speech corpora and a description of a new multimodal corpus of English speech recordings is provided. The new corpus containing 31 hours of recordings was created specifically to assist audio-visual speech recognition systems (AVSR) development. The database related to the corpus includes high-resolution, high-framerate stereoscopic video streams from RGB cameras, depth imaging stream utilizing Time-of-Flight camera accompanied by audio recorded using both: a microphone array and a microphone built in a mobile computer. For the purpose of applications related to AVSR systems training, every utterance was manually labeled, resulting in label files added to the corpus repository. Owing to the inclusion of recordings made in noisy conditions the elaborated corpus can also be used for testing robustness of speech recognition systems in the presence of acoustic background noise. The process of building the corpus, including the recording, labeling and post-processing phases is described in the paper. Results achieved with the developed audio-visual automatic speech recognition (ASR) engine trained and tested with the material contained in the corpus are presented and discussed together with comparative test results employing a state-of-the-art/commercial ASR engine. In order to demonstrate the practical use of the corpus it is made available for the public use.

/pdf/an-audio-visual-corpus-for-multimodal-automatic-speech-187vml2bes.pdf

An audio-visual corpus for multimodal automatic speech recognition

The process of counting stuttering events could be carried out more objectively through the automatic detection of stop-gaps, syllable repetitions and vowel prolongations. The alternative would be based on the subjective evaluations of speech fluency and may be dependent on a subjective evaluation method. Meanwhile, the automatic detection of intervocalic intervals, stop-gaps, voice onset time and vowel durations may depend on the speaker and the rules derived for a single speaker might be unreliable when trying to consider them as universal ones. This implies that learning algorithms having strong generalization capabilities could be applied to solve the problem. Nevertheless, such a system requires vectors of parameters, which characterize the distinctive features in a subject's speech patterns. In addition, an appropriate selection of the parameters and feature vectors while learning may augment the performance of an automatic detection system.

The paper reports on automatic recognition of stuttered speech in normal and frequency altered feedback speech. It presents several methods of analyzing stuttered speech and describes attempts to establish those parameters that represent stuttering event. It also reports results of some experiments on automatic detection of speech disorder events that were based on both rough sets and artificial neural networks.

/pdf/intelligent-processing-of-stuttered-speech-17ylcmrju9.pdf

Intelligent Processing of Stuttered Speech

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be employed in surveillance systems to monitor behavior of public events participants. The results can be used to detect sound source position in real time or to calculate the spatial distribution of sound energy in the environment. Moreover, the spatial filtration can be performed to separate sounds arriving from a chosen direction.

/pdf/detection-and-localization-of-selected-acoustic-events-in-1tlyfyup0a.pdf

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

Evaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier are introduced. The sound source localization algorithm based on the analysis of multichannel signals from the Acoustic Vector Sensor is presented. The methods are evaluated in an experiment conducted in the anechoic chamber, in which the representative events are played together with noise of differing intensity. The results of detection, classification and localization accuracy with respect to the Signal to Noise Ratio are discussed. The results show that the recognition and localization accuracy are strongly dependent on the acoustic conditions. We also found that the engineered algorithms provide a sufficient robustness in moderately intense noise in order to be applied to practical audio-visual surveillance systems.

/pdf/detection-classification-and-localization-of-acoustic-events-1043ws6m93.pdf

Andrzej Czyzewski

Papers

Representing Musical Instrument Sounds for Their Automatic Classification

An audio-visual corpus for multimodal automatic speech recognition

Intelligent Processing of Stuttered Speech

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations