EMIR: A novel emotion-based music retrieval system

Music is inherently expressive of emotion meaning and affects the mood of people. In this paper, we present a novel EMIR (Emotional Music Information Retrieval) System that uses latent emotion elements both in music and non-descriptive queries (NDQs) to detect implicit emotional association between users and music to enhance Music Information Retrieval (MIR). We try to understand the latent emotional intent of queries via machine learning for emotion classification and compare the performance of emotion detection approaches on different feature sets. For this purpose, we extract music emotion features from lyrics and social tags crawled from the Internet, label some for training and model them in high-dimensional emotion space and recognize latent emotion of users by query emotion analysis. The similarity between queries and music is computed by verified BM25 model

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Retrieval and Annotation of Music Using Latent Semantic Models

PhDThis thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences Research Counci

Sequential Complexity as a Descriptor for Musical Similarity

We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio