Emilia Gómez

Melody Extraction From Polyphonic Music Signals Using Pitch Contour Characteristics

We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed.

Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches.

Melody Transcription From Music Audio: Approaches and Evaluation

Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. We discuss melody-roughly, the part a listener might whistle or hum-as one such reduced descriptor of music audio, and consider how to define it, and what use it might be. We go on to describe the results of full-scale evaluations of melody transcription systems conducted in 2004 and 2005, including an overview of the systems submitted, details of how the evaluations were conducted, and a discussion of the results. For our definition of melody, current systems can achieve around 70% correct transcription at the frame level, including distinguishing between the presence or absence of the melody. Melodies transcribed at this level are readily recognizable, and show promise for practical applications

Audio Cover Song Identification and Similarity: Background, Approaches, Evaluation, and Beyond

A cover version is an alternative rendition of a previously recorded song. Given that a cover may differ from the original song in timbre, tempo, structure, key, arrangement, or language of the vocals, automatically identifying cover songs in a given music collection is a rather difficult task. The music information retrieval (MIR) community has paid much attention to this task in recent years and many approaches have been proposed. This chapter comprehensively summarizes the work done in cover song identification while encompassing the background related to this area of research. The most promising strategies are reviewed and qualitatively compared under a common framework, and their evaluation methodologies are critically assessed. A discussion on the remaining open issues and future lines of research closes the chapter.

Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges

Melody extraction algorithms aim to produce a sequence of frequency values corresponding to the pitch of the dominant melody from a musical recording. Over the past decade, melody extraction has emerged as an active research topic, comprising a large variety of proposed algorithms spanning a wide range of techniques. This article provides an overview of these techniques, the applications for which melody extraction is useful, and the challenges that remain. We start with a discussion of ?melody? from both musical and signal processing perspectives and provide a case study that interprets the output of a melody extraction algorithm for specific excerpts. We then provide a comprehensive comparative analysis of melody extraction algorithms based on the results of an international evaluation campaign. We discuss issues of algorithm design, evaluation, and applications that build upon melody extraction. Finally, we discuss some of the remaining challenges in melody extraction research in terms of algorithmic performance, development, and evaluation methodology.

Semantic audio content-based music recommendation and visualization based on user preference examples

Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the users musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the users musical preferences by creating a humanoid cartoon-like character - the Musical Avatar - automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-of-the-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems.

ESSENTIA: an open-source library for sound and music analysis

We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications.

Audio Watermarking and Fingerprinting: For Which Applications?

Although not a new issue, music piracy has acquired a new status in the digital era, as recordings can be easily copied and distributed. Watermarking has been proposed as a solution to this problem. It consists in embedding into the audio signal an inaudible mark containing copyright information. A different approach, called fingerprinting, consists in extracting a “fingerprint” from the audio signal. In association with a database, this fingerprint can be used to identify a recording, which is useful, for example, to monitor audio excerpts played by broadcasters and webcasters. There are far more applications to watermarking and fingerprinting. After a brief technical review, this article describes potential applications of both methodologies, showing which one is more suitable for each application.

Musical genre classification using melody features extracted from polyphonic music signals

We present a new method for musical genre classification based on high-level melodic features that are extracted directly from the audio signal of polyphonic music. The features are obtained through the automatic characterisation of pitch contours describing the predominant melodic line, extracted using a state-of-the-art audio melody extraction algorithm. Using standard machine learning algorithms the melodic features are used to classify excerpts into five different musical genres. We obtain a classification accuracy above 90% for a collection of 500 excerpts, demonstrating that successful classification can be achieved using high-level melodic features that are more meaningful to humans compared to low-level features commonly used for this task. We also compare our method to a baseline approach using low-level timbre features, and study the effect of combining these low-level features with our high-level melodic features. The results demonstrate that complementing low-level features with high-level melodic features is a promising approach.

Monoaural audio source separation using deep convolutional neural networks

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.