A music retrieval system is introduced that incorporate tempo, cultural, and beat strength features to help music therapists provide appropriate music for gait training for Parkinson's patients. Unlike current methods available to music therapists (e.g., personal CD/MP3 library search) we propose a domain-specific search engine that utilizes database of music found on YouTube. We independently evaluate the efficacy of our tempo, cultural, and beat strength features on a music database extracted from YouTube. Results from our user study demonstrate the effectiveness and usefulness of our search engine for this application.

Social tagging can provide rich semantic information for large-scale retrieval in music discovery. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Many of these are underrepresented by current tag recommenders. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. Our system is designed for large-scale deployment, on the order of millions of objects. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. We evaluate our tag recommendation system on CAL-500 and a large-scale data set ($N = 77,448$ songs) generated by crawling Youtube and Last.fm. Our results indicate that our proposed method is both effective for recommending attribute-diverse relevant tags and efficient at scalable processing.

Music classification based on cultural style is useful for music analysis and has potential applications in retrieval and recommendation systems. In this paper, we present the first attempt to classify audio signals automatically according to their cultural styles, which are characterized by timbre, rhythm, wavelet coefficients and musicology-based features. Machine learning algorithms are employed to investigate the effectiveness of various features on a data set of 1300 music pieces. Experimental results show that the proposed method can achieve an overall accuracy of 86% for six cultural styles, which shows the feasibility of integrating cultural style classification into music retrieval systems.

The combination of heterogeneous knowledge sources has been widely regarded as an effective approach to boost retrieval accuracy in many information retrieval domains. While various technologies have been recently developed for information retrieval, multimodal music search has not kept pace with the enormous growth of data on the Internet. In this paper, we study the problem of integrating multiple online information sources to conduct effective query dependent fusion (QDF) of multiple search experts for music retrieval. We have developed a novel framework to construct a knowledge space of users' information need from online folksonomy data. With this innovation, a large number of comprehensive queries can be automatically constructed to train a better generalized QDF system against unseen user queries. In addition, our framework models QDF problem by regression of the optimal combination strategy on a query. Distinguished from the previous approaches, the regression model of QDF (RQDF) offers superior modeling capability with less constraints and more efficient computation. To validate our approach, a large scale test collection has been collected from different online sources, such as Last.fm, Wikipedia, and YouTube. All test data will be released to the public for better research synergy in multimodal music search. Our performance study indicates that the accuracy, efficiency, and robustness of the multimodal music search can be improved significantly by the proposed folksonomy-RQDF approach. In addition, since no human involvement is required to collect training examples, our approach offers great feasibility and practicality in system development.

How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems in music information retrieval. This paper demonstrates a novel multimodal and adaptive music similarity measure (CompositeMap) with its application in a personalized multimodal music search system. CompositeMap can effectively combine music properties from different aspects into compact signatures via supervised learning, which lays the foundation for effective and efficient music search. In addition, an incremental Locality Sensitive Hashing algorithm is developed to support more efficient search processes. Experimental results based on two large music collections reveal various advantages in effectiveness, efficiency, adaptiveness, and scalability of the proposed music similarity measure and the music search system.

With the continuing advances in data storage and communication technology, there has been an explosive growth of music information from different application domains. As an effective technique for organizing, browsing, and searching large data collections, music information retrieval is attracting more and more attention. How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems. In this paper, we introduce a novel framework based on a multimodal and adaptive similarity measure for various applications. Distinguished from previous approaches, our system can effectively combine music properties from different aspects into a compact signature via supervised learning. In addition, an incremental Locality Sensitive Hashing algorithm has been developed to support efficient retrieval processes with different kinds of queries. Experimental results based on two large music collections reveal various advantages of the proposed framework including effectiveness, efficiency, adaptiveness, and scalability.