Luis Gustavo Martins

Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs

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.

Normalized Cuts for Predominant Melodic Source Separation

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.

MarsyasX: multimedia dataflow processing with implicit patching

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.

A Computationally Efficient Scheme for Dominant Harmonic Source Separation

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.

Speaker Segmentation of Interviews Using Integrated Video and Audio Change Detectors

In this paper, we study the use of audio and visual cues to perform speaker segmentation of audiovisual recordings of formal meetings such as interviews, lectures, or courtroom sessions. The sole use of audio cues for such recordings can be ineffective due to low recording quality and high level of background noise. We propose to use additional cues from the video stream by exploiting the relative static locations of speakers among the scene. The experiments show that the combination of those multiple cues helps to identify more robustly the transitions among speakers.

A framework for sound source separation using spectral clustering

Clustering based on the normalized cut criterion, and more generally, spectral clustering methods, are techniques originally proposed to model perceptual grouping tasks, such as image segmentation in computer vision. In this work, it is shown how such techniques can be applied to the problem of dominant melodic source separation in polyphonic music audio signals. One of the main advantages of this approach is the ability to incorporate mutiple perceptually‐inspired grouping criteria into a single framework without requiring multiple processing stages, as many existing computational auditory science analysis approaches do. Experimental results for several tasks, including dominant melody pitch detection, are presented. The system is based on a sinusoidal modeling analysis front‐end. A novel similarity cue based on harmonicity (harmonically‐wrapped peak similariy) is also introduced. The proposed system is data‐driven (i.e., requires no prior knowledge about the extracted source), causal, robust, practical,...