Dimitrios Bountouridis

Designing Games with a Purpose for Data Collection in Music Research. Emotify and Hooked: Two Case Studies

Collecting ground truth data for music research requires large amounts of time and money. To avoid these costs, researchers are now trying to collect information through online multiplayer games with the underlying purpose of collecting scientific data. In this paper we present two case studies of such games created for data collection in music information retrieval (MIR): Emotify, for emotional annotation of music, and Hooked, for studying musical catchiness. In addition to the basic requirement of scientific validity, both applications address essential development and design issues, for example, acquiring licensed music or employing popular social frameworks. As such, we hope that they may serve as blueprints for the development of future serious games, not only for music but also for other humanistic domains. The pilot launch of these two games showed that their models are capable of engaging participants and supporting large-scale empirical research.

Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles

The automated retrieval of related music documents, such as cover songs or folk melodies belonging to the same tune, has been an important task in the field of Music Information Retrieval (MIR). Yet outlier detection, the process of identifying those documents that deviate significantly from the norm, has remained a rather unexplored topic. Pairwise comparison of music sequences (e.g. chord transcriptions, melodies), from which outlier detection can potentially emerge, has been always in the center of MIR research but the connection has remained uninvestigated. In this paper we firstly argue that for the analysis of musical collections of sequential data, outlier detection can benefit immensely from the advantages of Multiple Sequence Alignment (MSA). We show that certain MSA-based similarity methods can better separate inliers and outliers than the typical similarity based on pairwise comparisons. Secondly, aiming towards an unsupervised outlier detection method that is data-driven and robust enough to be generalizable across different music datasets, we show that ensemble approaches using an entropy-based diversity measure can outperform supervised alternatives.

A data-driven approach to chord similarity and chord mutability

Assessing the relationship between chord sequences is an important ongoing research topic in the fields of music cognition and music information retrieval. Heuristic and cognitive models of chord similarity have been investigated but none has aimed to capture the collective perception of chord similarity from a large dataset of user-generated content. Devising a largescale experiment to gather sufficient data from human subjects has always been a major stumbling block. We present a novel chord similarity model based on a large amount of crowd-sourced transcriptions from a popular automatic chord estimation service. We show that our model outperforms heuristic-based models in a song identification task. Secondly, a model of chord mutations based on a large amount of crowd-sourced cover songs transcriptions is introduced. From crowd-sourced data, we create substitution matrices that capture the perceived similarity and mutability between chords. These results show that modelling the collective perception can not only substitute alternative, sophisticated models but also further enhance performance in various music information retrieval tasks.

Towards Polyphony Reconstruction Using Multidimensional Multiple Sequence Alignment

The digitization of printed music scores through the process of optical music recognition is imperfect. In polyphonic scores, with two or more simultaneous voices, errors of duration or position can lead to badly aligned and inharmonious digital transcriptions. We adapt biological sequence analysis tools as a post-processing step to correct the alignment of voices. Our multiple sequence alignment approach works on multiple musical dimensions and we investigate the contribution of each dimension to the correct alignment. Structural information, such musical phrase boundaries, is of major importance; therefore, we propose the use of the popular bioinformatics aligner Mafft which can incorporate such information while being robust to temporal noise. Our experiments show that a harmony-aware Mafft outperforms sophisticated, multidimensional alignment approaches and can achieve near-perfect polyphony reconstruction.

Melody Retrieval and Classification Using Biologically-Inspired Techniques

Retrieval and classification are at the center of Music Information Retrieval research. Both tasks rely on a method to assess the similarity between two music documents. In the context of symbolically encoded melodies, pairwise alignment via dynamic programming has been the most widely used method. However, this approach fails to scale-up well in terms of time complexity and insufficiently models the variance between melodies of the same class. Compact representations and indexing techniques that capture the salient and robust properties of music content, are increasingly important. We adapt two existing bioinformatics tools to improve the melody retrieval and classification tasks. On two datasets of folk tunes and cover song melodies, we apply the extremely fast indexing method of the Basic Local Alignment Search Tool (BLAST) and achieve comparable classification performance to exhaustive approaches. We increase retrieval performance and efficiency by using multiple sequence alignment algorithms for locating variation patterns and profile hidden Markov models for incorporating those patterns into a similarity model.

Tonic: Combining Ranking and Clustering Dynamics for Music Discovery

This paper describes the design of Tonic, a novel web interface for music discovery and playlist creation. Tonic maps songs into a two dimensional space using a combination of free tags, metadata, and audio-derived features. Search results are presented in this two dimensional space using a combination of clustering and ranking visualization strategies. Tonic was ranked first in the 2014 MIREX User Experience Grand Challenge, where it was evaluated in terms of learnability, robustness and overall user satisfaction, amongst others.