This survey paper provides an overview of content-based music information retrieval systems, both for audio and for symbolic music notation. Matching algorithms and indexing methods are briefly presented. The need for a TREC-like comparison of matching algorithms such as MIREX at ISMIR becomes clear from the high number of quite different methods which so far only have been used on different data collections. We placed the systems on a map showing the tasks and users for which they are suitable, and we find that existing content-based retrieval systems fail to cover a gap between the very general and the very specific retrieval tasks.

Nowadays, music fans are beginning to massively use mobile digital music players and dedicated software to organize and play large collections of music. In this context, users deal with huge music libraries containing thousands of tracks. Such a huge volume of music easily overwhelms users when selecting the music to listen or when organizing their collections. Music player software with visualizations based on textual lists and organizing features such as smart playlists are not really enough for helping users to efficiently manage their libraries. Thus, we propose new graphical visualizations and their associated features to allow users to better organize their personal music libraries and therefore also to ease selection later on. 1.

Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. To overcome this, the combination of multiple different feature sets bearing diverse characteristics is needed. We propose a new approach to extend the scope of the features: We transcribe audio data into a symbolic form using a transcription system, extract symbolic descriptors from that representation and combine them with audio features. With this method, we are able to surpass the glass ceiling and to further improve music genre classification, as shown in the experiments through three reference music databases and comparison to previously published performance results. 1

For tasks like musical genre identification and similarity searches in audio databases, audio files have to be described by suitable feature sets. Since these feature sets usually try to capture diverse discriminative characteristics, it is interesting and desirable to create an acoustic representation of the feature set to support intuitive evaluation. In this paper, we present an approach for making a specific feature set, namely Rhythm Patterns, instantly human comprehensible by re-assembling sound from the numerical descriptors. The re-synthesized audio chunks represent clearly perceivable rhythmical characteristics on critical frequency bands of the original music.

Because of the increasing number of music distributors offering an ever growing number of albums and tracks on the Internet, access methods such as retrieval, interactive exploration or similaritybased search demand more sophisticated technologies than metadatabased approaches currently o#er including queries for artists, albums, titles or manually assigned genre information. This requirement also holds for private collections with the tracks being more and more often stored uniformly in a library of, e.g., MP3 or OGG files as opposed to just a few tracks stored on separate media such as vinyl records, tapes or compact discs that have to be changed when playing more than one album.

With increasing amounts of audio being stored and distributed electronically, intuitive and efficient access to large music collections is becoming crucial. To this end we are developing algorithms for audio feature extraction, allowing to compute acoustic similarity between pieces of music, as well as tools utilizing this information to support retrieval of as well as navigation in music repositories. This paper provides an overview of the Rhythm Patterns feature set and demonstrates its suitability for music genre recognition. Furthermore, it outlines the principles of organizing digital music repositories using Self-Organizing Maps and presents the novel PlaySOM interface and the PocketSOMPlayer for mobile devices, both providing intuitively explorable music information spaces.

Drum samples are an important ingredient for many styles of music. Large libraries of drum sounds are readily available. However, their value is limited by the ways in which users can explore them to retrieve sounds. The available schemes for organizations those collections are either insufficient to hold a large number of samples, rely on cumbersome manual classification, are inconsistent, and error-prone. In this paper, we present a new...

Navigation in and access to the contents of digital audio archives have become increasingly important topics in Information Retrieval. Both private and commercial music collections are growing both in terms of size and acceptance in the user community. Content based approaches relying on signal processing techniques have been used in Music Information Retrieval for some time to represent the acoustic characteristics of pieces of music, which may be used for collection organisation or retrieval tasks. However, music is not defined by acoustic characteristics only, but also, sometimes even to a large degree, by its contents in terms of lyrics. A song’s lyrics provide more information to search for or may be more representative of specific musical genres than the acoustic content, e.g. ‘love songs ’ or ‘Christmas carols’. We therefore suggest an improved indexing of audio files by two modalities. Combinations of audio features and song lyrics can be used to organise audio collections and to display them via map based interfaces. Specifically, we use Self-Organising Maps as visualisation and interface metaphor. Separate maps are created and linked to provide a multi-modal view of an audio collection. Moreover, we introduce quality measures for quantitative validation of cluster spreads across the resulting multiple topographic mappings provided by the Self-Organising Maps.

The aim of this paper is to present an overview of the current situation in this hot topic of Multimedia Information Retrieval: Personalization. We are considering several aspects of this problem. On one hand, the user will want to have a personalized access to his image/video collections and this can be achieved by providing intuitive and natural browsing capabilities and customized features. Furthermore, the system is required to perform user profiling and to adapt the existing parameters of the system to the user needs and interest. On the other hand, it is also important to consider the devices and applications in which this technology is going to be deployed. Mobile media is a high growth area but the state-of-the-art technologies are lagging behind the consumers expectations. We are addressing in this paper precisely these three important aspects.

With the advent of larger electronic music repositories, the automatic organization of music into different genre categories is receiving increased attention. The creation of such genre hierarchies, as well as ways for providing useful interfaces to these, poses an interesting challenge. With the SOM-enhanced JukeBox (SOMeJB) system we developed an approach for automatically organizing pieces of music in raw audio format according to their perceived sound similarity based on Rhythm Patterns. This paper reviews the SOMeJB system and presents performance evaluations of the resulting hierarchical genre clusters. We furthermore compare the Rhythm Pattern based clustering with cluster structures obtained from other sets of features employed for genre-based music analysis. We demonstrate results based on a collection of about 24 hours of music.