We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.

https://repositori.upf.edu/bitstream/10230/27565/1/Urbano_ftir_mus.pdf

Music Information Retrieval: Recent Developments and Applications

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.

/pdf/sentiment-analysis-on-tweets-about-diabetes-an-aspect-level-1wl84is02k.pdf

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach.

Financial news plays a significant role with regard to predicting the behaviour of financial markets. However, the exponential growth of financial news on the Web has led to a need for new technolo...

Feature-based opinion mining in financial news: An ontology-driven approach

Time-scale modification (TSM) is the task of speeding up or slowing down an audio signal’s playback speed without changing its pitch. In digital music production, TSM has become an indispensable tool, which is nowadays integrated in a wide range of music production software. Music signals are diverse—they comprise harmonic, percussive, and transient components, among others. Because of this wide range of acoustic and musical characteristics, there is no single TSM method that can cope with all kinds of audio signals equally well. Our main objective is to foster a better understanding of the capabilities and limitations of TSM procedures. To this end, we review fundamental TSM methods, discuss typical challenges, and indicate potential solutions that combine different strategies. In particular, we discuss a fusion approach that involves recent techniques for harmonic-percussive separation along with time-domain and frequency-domain TSM procedures.

A Review of Time-Scale Modification of Music Signals †

This paper proposes an automatic DJ mixing method that can automate the processes of real world DJs and describes a prototype for a fully automatic DJ mix-like playing system. Our goal is to achieve a fully automatic DJ mixing system that can preserve overall user comfort level during DJ mixing. In this paper, we assume that the difference between the original and adjusted songs is the main cause of user discomfort in the mixed song. In order to preserve user comfort, we define the measurement function of user discomfort based on the results of a subjective experiment. Furthermore, this paper proposes a unique tempo adjustment technique called “optimal tempo adjustment”, which is robust for any combination of tempi of songs to be mixed. In the subjective experiment, the proposed method obtained higher averages of user ratings on three evaluation items compared to the conventional method. These results indicate that our system is able to preserve user comfort.

/pdf/full-automatic-dj-mixing-system-with-optimal-tempo-1320bda5tu.pdf

Full-automatic dj mixing system with optimal tempo adjustment based on measurement function of user discomfort

Feature based sentiment analysis is normally conducted using review Web sites, since it is difficult to extract accurate product features from tweets. However, Twitter users express sentiment towards a large variety of products in many different languages. Besides, sentiment expressed on Twitter is more up to date and represents the sentiment of a larger population than review articles. Therefore, we propose a method that identifies product features using review articles and then conduct sentiment analysis on tweets containing those features. In that way, we can increase the precision of feature extraction by up to 40% compared to features extracted directly from tweets. Moreover, our method translates and matches the features extracted for multiple languages and ranks them based on how frequently the features are mentioned in the tweets of each language. By doing this, we can highlight the features that are the most relevant for multilingual analysis.

Feature Based Sentiment Analysis of Tweets in Multiple Languages

This research presents a formal user evaluation of a typical visualization method for content-based music information retrieval (MIR) systems, and also proposes a novel interface to improve MIR usability. Numerous interfaces to visualize content-based MIR systems have been proposed, but reports on user evaluations of such proposed GUIs are scarce. This research aims to evaluate the effectiveness of a typical 2-D visualization method for content-based MIR systems, by conducting comparative user evaluations against the traditional list-based format to present MIR results to the user. Based on the observations of the experimental results, we next propose a 3-D visualization system, which features a function to specify sub-regions of the feature space based on genre classication results, and a function which allows users to select features that are assigned to the axes of the 3-D space. Evaluation of this GUI conclude that the functions of the 3-D system can significantly improve both the efcienc y and usability of MIR systems.

/pdf/usability-evaluation-of-visualization-interfaces-for-content-5fepmnace2.pdf

Usability evaluation of visualization interfaces for content-based music retrieval systems

In order to achieve highly accurate content-based music information retrieval (MIR), it is necessary to compensate the various bit rates of encoded songs which are stored in the music collection, since the bit rate differences are expected to apply a negative effect to content-based MIR results. In this paper, we examine how the bit rate differences affect MIR results, propose methods to normalize MFCC features extracted from encoded files with various bit rates, and show their effects to stabilize MIR results.

/pdf/feature-analysis-and-normalization-approach-for-robust-31g164w5x8.pdf

Feature Analysis and Normalization Approach for Robust Content-Based Music Retrieval to Encoded Audio with Different Bit Rates

Degrees of association between categories of songs (hereinafter, song categories) corresponding to information showing advertisement distribution target categories and distribution object advertisements are determined in advance in an advertisement DB. A song categorization module calculates degrees of association between an acoustic feature amount of a song being played (hereinafter, current song) and feature amounts of the song categories, and a song-related distribution object advertisement candidate group extraction module determines a distribution object advertisement corresponding to a song category with a high degree of association is determined as a distribution object advertisement. An advertisement presentation module provides information on the distribution object advertisement to a user along with current song information.

Hiromi Ishizaki

Papers

Full-automatic dj mixing system with optimal tempo adjustment based on measurement function of user discomfort

Feature Based Sentiment Analysis of Tweets in Multiple Languages

Usability evaluation of visualization interfaces for content-based music retrieval systems

Feature Analysis and Normalization Approach for Robust Content-Based Music Retrieval to Encoded Audio with Different Bit Rates

Music-linked advertisement distoribution method, device, and system