Paraskevi S. Lampropoulou

A Cascade-Hybrid Music Recommender System for mobile services based on musical genre classification and personality diagnosis

In this paper, we present a Cascade-Hybrid Music Recommender System intended to operate as a mobile service. Specifically, our system is a middleware that realizes the recommendation process based on a combination of music genre classification and personality diagnosis. A mobile user is able to query for music files by simply sending an example music file from his/her mobile device. In response to the user query, the system recommends music files that not only belong to the same genre as the user query, but also an attempt has been made to take into account both the user preferences as well as ratings from other users for candidate results. The recommendation mechanism is realized by applying the collaborative filtering technique of personality diagnosis. Using the minimum absolute error and the ranked scoring criteria, our approach is compared to existing recommendation techniques that rely on either collaborative filtering or content-based approaches. The outcome of the comparison clearly indicates that our approach exhibits significantly higher performance.

ALIMOS: a middleware system for a ccessing digital music LI braries in MO bile s ervices

We present a software tool, called ALIMOS, for accessing digital music libraries in mobile telecommunication services. The tool provides a semi-automatic interface that allows users to interact with their digital music library in a flexible way based on a combination of content and semantic information. The system architecture is based on a multi-tier application with a front-end and a back-end level. ALIMOS is fully implemented and relevant evaluation results are given.

Music genre classification based on ensemble of signals produced by source separation methods

In this paper, we present a system for musical genre classification that uses a preprocessing module to separate corresponding audio signals into three source signals. A feature extraction procedure is applied to each separated signal and the extracted features are fed into an ensemble combination of Support Vector Machine-based classifiers for genre classification. For the source separation task, we examine and compare two relevant algorithms, namely Convolutive Sparse Coding and a Wavelet Packets-based algorithm. We evaluate our system on a music database of four hundred music samples from four different music genres. Experimental results show that there is a higher classification accuracy in applying a source separation algorithm before feature extraction.

Evaluation of a Middleware System for Accessing Digital Music Libraries in Mobile Services

In this paper, we present and discuss the evaluation process of a middleware system that we have developed. Our system facilitates the access to digital music libraries in push technology-based mobile services. Specifically, our system provides a semi-automatic interface that allows users to interact with a digital music library and find/retrieve music files in a flexible way based on a combination of mobile technologies and content-based retrieval techniques. The evaluation process followed three stages, namely user background information collection, system performance evaluation, and overall system assessment. The evaluation results are quite positive in terms of both system performance evaluation, and overall system assessment.

Musical Instrument Category Discrimination Using Wavelet-Based Source Separation

In this paper, we present and evaluate a new innovative method for quantitative estimation of the musical instrument categories which compose a music piece. The method uses a wavelet-based music source (i.e., musical instrument) separation algorithm and consists of two steps. In the first step, a source separation technique based on wavelet packets is applied to separate the musical instruments which compose a music piece. In the second step, a classification algorithm based on support vector machines is applied to estimate the musical category of each of the musical instruments identified in the first step. The method is evaluated on the publically available Iowa Musical Instrument Database and found to perform quite successfully.

A Mobile Music Recommender System based on a Two-level Genre-Rating SVM Classifier enhanced by Collaborative Filtering

In this paper, we present a mobile recommender system for music. The system acts as middleware that facilitates the recommendation in push technologybased mobile services and combines content-based retrieval and collaborative filtering. Specifically, a mobile user is provided with the ability to query for music files that belong to the same genre by simply sending an example music file from his/her mobile device. The system recommends to the user music files not only on the basis of acoustic content similarity, but also incorporates collaborative filtering techniques in order to offer more personalized recommendations.

Evaluation Process and Results of a Middleware System for Accessing Digital Music LIbraries in MObile Services

In this paper, we present and discuss the evaluation process of a middleware system that we have developed to facilitate the access to digital music libraries in push technology-based mobile services. Our system combines mobile technologies and content-based retrieval techniques to provide a semi-automatic interface that allows users to interact with a digital music library and find/retrieve music files in a flexible way. To evaluate our system, we followed a three stage process, namely user background information collection, system performance evaluation, and overall system assessment. The evaluation results are quite positive in terms of both system performance evaluation, and overall system assessment.

A Back End System for Digital Image Library Organization Based on Concept Learning

We present a back-end system which organizes digital image libraries according to a user-defined concept. The concept is extracted from a set of images that the user submits to the system as its representative instances. A relevance feedback procedure, implemented with SVM-based incremental learning algorithms, tunes a classifier that discriminates between concept-relevant and concept-irrelevant images. The system is fully developed and experimentally tested with success.