An important task in Music Information Retrieval is content-based similarity retrieval in which given a query music track, a set of tracks that are similar in terms of musical content are retrieved. A variety of audio features that attempt to model different aspects of the music have been proposed. In most cases the resulting audio feature vector used to represent each music track is high dimensional. It has been observed that high dimensional music similarity spaces exhibit some anomalies: hubs which are tracks that are similar to many other tracks, and orphans which are tracks that are not similar to most other tracks. These anomalies are an artifact of the high dimensional representation rather than actually based on the musical content. In this work we describe a distance normalization method that is shown to reduce the number of hubs and orphans. It is based on post-processing the similarity matrix that encodes the pair-wise track similarities and utilizes clustering to adapt the distance normalization to the local structure of the feature space.

Music listeners frequently use words to describe music. Personalized music recommendation systems such as Last.fm and Pandora rely on manual annotations (tags) as a mechanism for querying and navigating large music collections. A well-known issue in such recommendation systems is known as the cold-start problem: it is not possible to recommend new songs/tracks until those songs/tracks have been manually annotated. Automatic tag annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. We describe how stacked generalization can be used to improve the performance of a state-of-the-art automatic tag annotation system for music based on audio content analysis and report results on two publicly available datasets.

The leading voice is an important feature of musical pieces and can often be considered as the dominant harmonic source. We propose in this paper a new scheme for the purpose of efficient dominant harmonic source separation. This is achieved by considering an harmonicity cue which is first compared with state-of-the-art cues using a generic evaluation methodology. The proposed separation scheme is then compared to a generic computational auditory scene analysis framework. Computational speed-up and performance comparison is done using source separation and music information retrieval tasks.

The design and implementation of multimedia signal processing systems is challenging especially when efficiency and real-time performance is desired. In many modern applications, software systems must be able to handle multiple flows of various types of multimedia data such as audio and video. Researchers frequently have to rely on a combination of different software tools for each modality to assemble proof-of-concept systems that are inefficient, brittle and hard to maintain. Marsyas is a software framework originally developed to address these issues in the domain of audio processing. In this paper we describe MarsyasX, a new open-source cross-modal analysis framework that aims at a broader score of applications. It follows a dataflow architecture where complex networks of processing objects can be assembled to form systems that can handle multiple and different types of multimedia flows with expressiveness and efficiency.

The predominant melodic source, frequently the singing voice, is an important component of musical signals. In this paper, we describe a method for extracting the predominant source and corresponding melody from ldquoreal-worldrdquo polyphonic music. The proposed method is inspired by ideas from computational auditory scene analysis. We formulate predominant melodic source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. Sinusoidal modeling is used as the underlying representation. A novel harmonicity cue which we term harmonically wrapped peak similarity is introduced. Experimental results supporting the use of this cue are presented. In addition, we show results for automatic melody extraction using the proposed approach.