Abstract. In this paper, we propose an audio-visual approach to video genre categorization. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal structural level, we asses action contents with respect to human perception. Further, color perception is quantified with statistics of color distribution, elementary hues, color properties and relationship of color. The last category of descriptors determines statistics of contour geometry. An extensive evaluation of this multi-modal approach based on on more than 91 hours of video footage is presented. We obtain average precision and recall ratios within [87% − 100%] and [77% − 100%], respectively, while average correct classification is up to 97%. Additionally, movies displayed according to feature-based coordinates in a virtual 3D browsing environment tend to regroup with respect to genre, which has potential application with real content-based browsing systems.

Abstract:In this paper we address the issue of automatic video genre categorization of web media using an audio-visual approach. To this end, we propose content descriptors which exploit audio, temporal structure and color information. The potential of our descriptors is experimentally validated both from the perspective of a classification system and as an information retrieval approach. Validation is carried out on a real scenario, namely on more than 288 hours of video footage and 26 video genres specific to blip.tv media platform. Additionally, to reduce semantic gap, we propose a new relevance feedback technique which is based on hierarchical clustering. Experimental tests prove that retrieval performance can be significantly increased in this case, becoming comparable to the one obtained with high level semantic textual descriptors. Abstract In this paper we address the issue of automatic video genre categorization of web media using an audio-visual approach. To this end, we propose content descriptors which exploit audio, temporal structure and color information. The potential of our descriptors is experimentally validated both from the perspective of a classification system and as an information retrieval approach. Validation is carried out on a real scenario, namely on more than 288 hours of video footage and 26 video genres specific to blip.tv media platform. Additionally, to reduce semantic gap, we propose a new relevance feedback technique which is based on hierarchical clustering. Experimental tests prove that retrieval performance can be significantly increased in this case, becoming comparable to the one obtained with high level semantic textual descriptors. Powered by Editorial Manager® and Preprint Manager® from Aries Systems Corporation

Abstract. In this paper two sets of evaluation experiments are conducted. First, we compare state-of-the-art automatic music genre classification algorithms to human performance on the same dataset, via a listening experiment. This will show that the improvements of contentbased systems over the last years have reduced the gap between automatic and human classification performance, but could not yet close this gap. As an important extension to previous work in this context, we will also compare the automatic and human classification performance to a collaborative approach. Second, we propose two evaluation metrics, called user scores, that are based on the votes of the participants of the listening experiment. This user centric evaluation approach allows to get rid of predefined ground truth annotations and allows to account for the ambiguous human perception of musical genre. To take genre ambiguities into account is an important advantage with respect to the evaluation of content-based systems, especially since the dataset compiled in this work (both the audio files and collected votes) are publicly available.

Abstract. This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.

No abstract

We propose three heuristics to determine the country of origin of a person or institution via text-based IE from the Web. We evaluate all methods on a collection of music artists and bands, and show that some heuristics outperform earlier work on the topic by terms of coverage, while retaining similar precision levels. We further investigate an extension using country-specific synonym lists.

The origin of a music artist or a band is an important kind of musical meta-data as it usually influences his/her/its music. In this paper, we propose three approaches to automatically determine the country of origin of a person or institution, which we apply to music artists and bands. The first approach investigates estimates of page counts returned for specific queries to Web search engines. The second approach uses term weighting functions for country-specific terms that occur on the top-ranked Web pages of an artist. The third approach applies to Web pages text distance measures between country-specific terms and key terms related to the concept or origin. We further present a thorough evaluation of the approaches taking into consideration different refinements. We show that we are able to outperform the first, nevertheless recent, approach to determine the origin of a music artist.

Abstract. We propose a new approach to a music search engine that can be accessed via natural language queries. As with existing approaches, we try to gather as much contextual information as possible for individual pieces in a (possibly large) music collection by means of Web retrieval. While existing approaches use this textual information to construct representations of music pieces in a vector space model, in this paper, we propose a document-centered technique to retrieve music pieces relevant to arbitrary natural language queries. This technique improves the quality of the resulting document rankings substantially. We report on the current state of the research and discuss current limitations, as well as possible directions to overcome them. Motivation and ContextWhile digital music databases contain several millions of audio pieces nowadays, indexing of these collections is in general still accomplished using a limited set of traditional meta-data descriptors like artist name, track name, album, or year. In most cases, also some sort of classification into coarse genres or different styles is available. Since this may not be sufficient for intuitive retrieval, several innovative (content-based) approaches to access music collections have been presented in the past years. However, the majority of these retrieval systems is based on query-by-example methods, i.e. the user must enter a query in a musical representation which is uncommon to most users and thus lacks acceptance. To address this issue, recently, different approaches to music search engines that can be accessed via textual queries have been proposed [4][5][6]9].In [6], we presented an approach that exploits contextual information related to the music pieces in a collection. To this end, tf × idf features are extracted from Web pages associated with the pieces and their corresponding artist. Furthermore, to represent audio pieces with no (or only little) Web information associated, also audio similarity is incorporated. This technique enables the user to issue queries like "rock with great riffs" to express the intention to find pieces that contain energetic guitar phrases instead of just finding tracks that have been labeled as rock by some authority. The general intention of the system presented in [6] is to allow for virtually any possible query and return the most appropriate pieces according to their "Web context" (comparable to e.g. Google's image search function).