Competences related to the use of digital and mobile technologies are of key importance as connected mobile devices such as smartphones or tablets become more and more pervasive and ubiquitous. In a moment where socio-demographic changes resulted in a rapidly growing number of senior citizens, it is fundamental to provide this target group with the necessary skills to be connected and integrated in this world preventing an age-related digital divide. As a contribution to that effort, a European-wide digital literacy development initiative for senior citizens was setup. This article presents the analysis of the achieved results which reflects a very positive perception of the seniors on the developed digital abilities.

We report on the tempo induction contest organized during the International Conference on Music Information Retrieval (ISMIR 2004) held at the University Pompeu Fabra in Barcelona, Spain, in October 2004. The goal of this contest was to evaluate some state-of-the-art algorithms in the task of inducing the basic tempo (as a scalar, in beats per minute) from musical audio signals. To our knowledge, this is the first published large scale cross-validation of audio tempo induction algorithms. Participants were invited to submit algorithms to the contest organizer, in one of several allowed formats. No training data was provided. A total of 12 entries (representing the work of seven research teams) were evaluated, 11 of which are reported in this document. Results on the test set of 3199 instances were returned to the participants before they were made public. Anssi Klapuri's algorithm won the contest. This evaluation shows that tempo induction algorithms can reach over 80% accuracy for music with a constant tempo, if we do not insist on finding a specific metrical level. After the competition, the algorithms and results were analyzed in order to discover general lessons for the future development of tempo induction systems. One conclusion is that robust tempo induction entails the processing of frame features rather than that of onset lists. Further, we propose a new "redundant" approach to tempo induction, inspired by knowledge of human perceptual mechanisms, which combines multiple simpler methods using a voting mechanism. Machine emulation of human tempo induction is still an open issue. Many avenues for future work in audio tempo tracking are highlighted, as for instance the definition of the best rhythmic features and the most appropriate periodicity detection method. In order to stimulate further research, the contest results, annotations, evaluation software and part of the data are available at http://ismir2004.ismir.net/ISMIR_Contest.html

We present the MusicSurfer, a metadata free system for the interaction with massive collections of music. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need for metadata nor human ratings.

We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music information retrieval systems. Moreover, a textual analysis of a song can generate ground truth data that can be used to validate results from purely acoustic methods. Preliminary results on language identification, structure extraction, categorization and similarity searches suggests that a lot of profit can be gained from the analysis of lyrics.

We present a metadata free system for the interaction with massive collections of music, the MusicSurfer. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need of metadata or human ratings.

Audio fingerprinting technologies allow the identification of audio content without the need of external meta-data or watermark embedding. These audio fingerprinting technologies work by extracting a content-based compact digest that summarizes a recording and comparing them with a previously extracted fingerprint database. In this paper we present a fingerprint scheme that is based on hidden Markov models. This approach achieves a high compaction of the audio signal by exploiting structural redundancies on music and robustness to distortions thanks to the stochastic modeling. In this paper we present the basic functionality of the system as well as some results

We report on a method of automatic extraction of a metrical attribute from percussive music audio signals: the smallest rhythmic pulse, called the ìtickî. The relevance of use of this feature in the framework of subsequent analyses is discussed and evaluated.