The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference

Daily Eastern News: October 19, 1990

https://thekeep.eiu.edu/den_1990_oct/1013/thumbnail.jp

Daily Eastern News: October 19, 1990

https://thekeep.eiu.edu/den_1990_oct/1013/thumbnail.jp

Analyzing Songs Used for Lyric Analysis With Mental Health Consumers Using Linguistic Inquiry and Word Count (LIWC) Software

Lyric analysis is one of the most commonly used music therapy interventions with the mental health population, yet there is a gap in the research literature regarding song selection. The primary purpose of this study was to determine distinguishing linguistic characteristics of song lyrics most commonly used for lyric analysis with mental health consumers, as measured by LIWC2015 software. A secondary purpose was to provide an updated song list resource for music therapists and music therapy students working with the mental health population. The researcher emailed a survey to 6,757 board-certified music therapists, 316 of whom completed the survey. Respondents contributed 700 different songs that they deemed most effective for lyric analysis with mental health consumers. The researcher used the LIWC2015 software to analyze the 48 songs that were listed by five or more music therapists. Song lyrics contained linguistic indicators of self-focused attention, present-focused attention, poor social relationships, and high cognitive processing. Lyrics were written in an informal, personal, and authentic style. Some lyrics were more emotionally positive, while others were more emotionally negative. While results must be interpreted with caution, it may be helpful to consider linguistic elements when choosing songs for lyric analysis with mental health consumers

Tracking Ensemble Performance on Touch-Screens with Gesture Classification and Transition Matrices

We present and evaluate a novel interface for tracking ensemble performances on touch-screens. The system uses a Random Forest classifier to extract touch-screen gestures and transition matrix statistics. It analyses the resulting gesture-state sequences across an ensemble of performers. A series of specially designed iPad apps respond to this real-time analysis of free-form gestural performances with calculated modifications to their musical interfaces. We describe our system and evaluate it through cross-validation and profiling as well as concert experienc

Washington University Record, November 8, 2002

https://digitalcommons.wustl.edu/record/1948/thumbnail.jp

Utah State University Commencement, 2013 – Main Campus

126th Annual Commencement of Utah State University.https://digitalcommons.usu.edu/commencement/1123/thumbnail.jp

An Approach for Intention-Driven, Dialogue-Based Web Search

Web search engines facilitate the achievement of Web-mediated tasks, including information retrieval, Web page navigation, and online transactions. These tasks often involve goals that pertain to multiple topics, or domains. Current search engines are not suitable for satisfying complex, multi-domain needs due to their lack of interactivity and knowledge. This thesis presents a novel intention-driven, dialogue-based Web search approach that uncovers and combines users\u27 multi-domain goals to provide helpful virtual assistance. The intention discovery procedure uses a hierarchy of Partially Observable Markov Decision Process-based dialogue managers and a backing knowledge base to systematically explore the dialogue\u27s information space, probabilistically refining the perception of user goals. The search approach has been implemented in IDS, a search engine for online gift shopping. A usability study comparing IDS-based searching with Google-based searching found that the IDS-based approach takes significantly less time and effort, and results in higher user confidence in the retrieved results

Mustang Daily, March 12, 2003

Student newspaper of California Polytechnic State University, San Luis Obispo, CA.https://digitalcommons.calpoly.edu/studentnewspaper/6992/thumbnail.jp