Piotr Hoffmann

Creating a Reliable Music Discovery and Recommendation System

The aim of this chapter is to show problems related to creating a reliable music discovery system. The SYNAT database that contains audio files is used for the purpose of experiments. The files are divided into 22 classes corresponding to music genres with different cardinality. Of utmost importance for a reliable music recommendation system are the assignment of audio files to their appropriate genres and optimum parameterization for music-genre recognition. Hence, the starting point is audio file filtering, which can only be done automatically, but to a limited extent, when based on low-level signal processing features. Therefore, a variety of parameterization techniques are shortly reviewed in the context of their suitability to music retrieval from a large music database. In addition, some significant problems related to choosing an excerpt of audio file for an acoustic analysis and parameterization are pointed out. Then, experiments showing results of searching for songs that bear the greatest resemblance to the song in a given query are presented. In this way music recommendation system may be created that enables to retrieve songs that are similar to each other in terms of their low-level feature description and genre inclusion. The experiments performed also provide basis for more general observations and conclusions.

Music Data Processing and Mining in Large Databases for Active Media

The aim of this paper was to investigate the problem of music data processing and mining in large databases. Tests were performed on a large database that included approximately 30000 audio files divided into 11 classes corresponding to music genres with different cardinalities. Every audio file was described by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable value of factors was employed. The tests were conducted in the WEKA application with the use of k-Nearest Neighbors (kNN), Bayesian Network (Net) and Sequential Minimal Optimization (SMO) algorithms. All results were analyzed in terms of the recognition rate and computation time efficiency.

Music Genre Recognition in the Rough Set-Based Environment

The aim of this paper is to investigate music genre recognition in the rough set-based environment. Experiments involve a parameterized music database containing 1100 music excerpts. The database is divided into 11 classes corresponding to music genres. Tests are conducted using the Rough Set Exploration System (RSES), a toolset for analyzing data with the use of methods based on the rough set theory. Classification effectiveness employing rough sets is compared against k-Nearest Neighbors (k-NN) and Local Transfer function classifiers (LTF-C). Results obtained are analyzed in terms of global class recognition and also per genre.

Pilot testing of developed multimodal biometric identity verification system

The bank client identity verification system developed in the course of the IDENT project is presented. The total number of five biometric modalities including: dynamic signature proofing, voice recognition, face image verification, face contour extraction and hand blood vessels distribution comparison have been developed and studied. The experimental data were acquired employing multiple biometric sensors installed at engineered biometric terminals. The biometric portraits of 125 subjects were registered and stored in the database during the presented pilot study and then verified experimentally. The analysis of FAR and FRR measures obtained for developed biometric applications was made. Problem-specific survey was done on the basis of questionnaires completed by the subjects in order to assess the look and feel of the developed biometric system as well as to collect opinions concerning its implementation in banking outlets. A discussion concerning the quality of registered signals and results achieved in the pilot study is included.

The Project IDENT: Multimodal Biometric System for Bank Client Identity Verification

Biometric identity verification methods are implemented inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. A secure communication system based on an intra-bank client-server architecture was designed for this purpose. Hitherto achieved progress within the project is reported in this paper with a focus on the design and implementation of the developed biometric authentication system. Implemented multimodal biometric client identity verification methods are briefly outlined and results of hitherto obtained biometric sample acquisition and analysis are reported.

Validating data acquired with experimental multimodal biometric system installed in bank branches

An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.

Classification of Music Genres by Means of Listening Tests and Decision Algorithms

The paper compares the results of audio excerpt assignment to a music genre obtained in listening tests and classification by means of decision algorithms. A short review on music description employing music styles and genres is given. Then, assumptions of listening tests to be carried out along with an online survey for assigning audio samples to selected music genres are presented. A framework for music parametrization is created resulting in feature vectors, which are checked for data redundancy. Finally, the effectiveness of the automatic music genre classification employing two decision algorithms is presented. Conclusions contain the results of the comparative analysis of the results obtained in listening tests and automatic genre classification.

Analysis of results of large-scale multimodal biometric identity verification experiment

An analysis of a large set of biometric data obtained during the enrolment and the verification phase in an experimental biometric system installed in bank branches is presented. Subjective opinions of bank clients and of bank tellers were also surveyed concerning the studied biometric methods in order to discover and to explore relations emerging from the obtained multimodal dataset. First, data acquisition and identity verification methods are described in this study. Then, relationships between ratios of successful and failed verifications between pairs, triplets, and quartets of biometric modalities are studied. An analysis of the sentiment of clients and of banking tellers related to each identity verification attempt was performed based on linguistic methods. The data mining process is described, based on the rough sets methodology, aimed at deriving rules pertaining to consecutive identity verification attempts.

Performance Analysis of Developed Multimodal Biometric Identity Verification System

The bank client identity verification system developed in the course of the IDENT project is presented. The total number of five biometric modalities including: dynamic handwritten signature proofing, voice recognition, face image verification, face contour extraction and hand blood vessels distribution comparison have been developed and studied. The experimental data were acquired employing multiple biometric sensors installed at engineered biometric terminals. The biometric portraits of more than 10 000 bank clients were registered and stored in the database during the presented study and then verified experimentally. Problem- specific survey was done on the basis of questionnaires completed by the subjects in order to assess the look and feel of the developed biometric system as well as to collect opinions concerning its implementation in banking outlets. A discussion concerning the quality of registered data and results achieved in the study is included.

Comparison of classification of musical genre obtained by subjective tests and decision algorithms

The aim of the study is to conduct subjective tests on audio excerpt assignment to music genre and to carry out automatic classification of musical genres with the use of decision algorithms. First, the musicology background of classifying music into styles and genres is discussed. Then, an online survey is created to perform subjective tests with a group of listeners, whose task is assigning audio samples to selected music genres. Next, a set of music descriptors is proposed and all music excerpts are parametrized. For checking parameter redundancy the Principal Component Analysis (PCA) is performed. The created database containing feature vectors is then utilized for automatic music genre classification. Two classifiers, namely: Belief Networks and SMO (Sequential Minimal Optimization Algorithm) are employed for the purpose of music genre classification. The last step of this study is to compare the efficiency of the listeners classification with the automatic music genre classification system designed ...