Abstract Computational modelling of music similarity is an increasingly impor- tant task for personalisation and optimisation in Music Information Retrieval and for research in music perception and cognition. Relative similarity ratings provide a new and promising approach to this task as they avoid problems associated with absolute ratings. In this article, we use relative ratings from the MagnaTagATune dataset to develop a complete learning and evaluation process with state-of-the- art algorithms and provide the first comprehensive and rigorous evaluation of this approach. We compare different high and low level audio features, genre data, di- mensionality effects, weighted similarity ratings, and different sampling methods. For model adaptation, we compare SVM-based metric learning, Metric-Learning- to-Rank (MLR), including a diagonal and a novel weighted MLR variant, and similarity learning with Neural Networks. Our results show that music similarity measures learnt on relative ratings are significantly better than a standard metric, depending on the choice of learning algorithm, feature set and application sce- nario. We implemented a testing framework in Matlab R , which we made publicly
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Abstract. In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the Mag- naTagATune database. Using common formats for feature data, our ap- proach can easily be transferred to other existing databases. Our results show a notable correlation between songs’ genres and associated similar- ity ratings, but learning on a combined feature set clearly outperforms either individual approach.
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Initial experiments in the use of the aforementioned content- based “timbral” music similarity techniques showed that the use of simple distance measurements between sets of fea- tures, or clusters of features, can produce a number of un- fortunate errors, despite generally good performance. Er- rors are often the result of confusion between superficially similar timbres of sounds, which a human listener might identify as being very dissimilar. A common example might be the confusion of a classical lute timbre, with that of an acoustic guitar string that might be found in folk, pop, or rock music. These two sounds are relatively close together in almost any acoustic feature space and might be identi- fied as similar by a na¨ıve listener, but would likely be placed very far apart by any listener familiar with western mu- sic. This may lead to the unlikely confusion of rock music with classical music, and the corruption of any playlist pro- duced.
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In order to support individual user perspectives and differ- ent retrieval tasks, music similarity can no longer be con- sidered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet dis- tance weighting on the MagnaTagATune dataset. A cross- validation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special at- tention is paid to the amount of training data necessary for making similarity predictions on unknown data, the num- ber of model parameters and the amount of information available about the music itself.
In the present paper, we apply general algorithms for met- ric learning to a music similarity modelling task Using sim- ple and widely available features and comparative similar- ity ratings, we demonstrated that a considerable proportion of the ratings can be effectively learned and reproduced us- ing Mahalanobis distances. This corroborates the initial hy- pothesis that the ratings sharing some concordant informa- tion. Whilst with both the original features and the low- dimensional PCA features the MLR algorithm shows supe- rior results, the diagonal matrix algorithms show compara- ble generalisation abilities for the PCA features. However, PCA seems not suitable for reducing feature dimensionality in a musical similarity context. Instead, the metric leaning techniques may hint on the necessary transformations and on which features may be ommitted.
Although simple linear search was suggested for per- sonal collections,  the size of our current database is over 150.000 tracks and it is expected to grow. For this reason, we partition the data space by similarity to form self-similar groups of songs. These groups or clusters can then be used to index the database. We can limit the search space by choosing the best matching cluster based on its proximity to the query song. Hence, the number of direct similarity calculations is greatly reduced. Since our goal is search optimisation rather than classification, we choose an unsupervised learning algorithm using a self-organising model, similar to a Self Organising Map . The details of this exceed the scope of our current discussion. However, it is important to note that using the symmetrised Kullback- Leibler (KL) divergence as basis for training and classifica- tion, we could verify the scarcity of hubs reported in  using a 100-times larger database of features. The songs are roughly equally distributed among the nodes. Only 4% of the nodes became hubs (containing a large set of songs) and 3% of them contain fewer songs. We also found that this phenomenon is largely independent of the size of the model (the number of nodes). The fact that the collec- tion can be partitioned automatically by grouping similar songs - without obtaining too many over-populated clus- ters (hubs) - shows that the database is well balanced and justifies the choice of metrics and learning algorithm. This is also favourable for the search application, since we can limit the number of songs where the similarity has to be ex- plicitly calculated and compute the divergence only within a single class without significantly modifying the results set. In our current implementation, a local copy of the par- titioned database is used for searching, however, the model is trained on the data available at the SPARQL end-point. This is achieved by an appropriate SPARQL query, gen- erated and issued in each training iteration. This way, the model can easily be adjusted if the database is expanded in the future. For producing the final results, a limited set of similar songs is collected and ranked by similarity to the query song(s) using the KL divergence described in . Finally, the metadata are obtained from MusicBrainz and displayed to the user.
Evalynd (Eve) Reed Kenyon (’75) teaches 6th grade chorus & general music at Acadia Middle School in Clifton Park, NY which is about 25 miles north of Albany, NY or 200 miles north of NYC. In February, five of her students were selected for the American Choral Directors Association All Eastern Conference Honors Choir & performed in Carnigie Hall. In addition to that, she has taken as many as 25 students to perform in the Macy's Thanksgiving Day Parade on four different occasions. Each spring, she takes her select singers to Lincoln Center to perform at the fountain area. Kenyon also directs a community choir for a Christmas musical and has been a church choir director for almost 30 years. Brenda Breland Sims (’81) is serving as an accompanist on staff at Hillcrest Baptist Church in Enterprise, Alabama and works as a contract employee at Enterprise High School as an accompanist in the choral department.
In March, flutist Jill Felber and collaborative pianist Diane Frazer visited the Department of Music to perform a recital of works from their newest CD, Fusion. Felber has performed solo recitals, chamber music, and concertos on four continents and has held residencies in Hong Kong, Taiwan, Australia, Mexico, France, Switzerland, Great Britain, Italy, Canada and the United States. She has premiered over three hundred works for the flute and has released world premiere recordings. In demand as a guest clinician because of her extraordinary motivational teaching style, Felber is currently professor of flute at the University of California, Santa Barbara.
The Delta State University Opera presented its fall production to a standing-room-only audience in December 2008. The excitement of the audience carried over to the performers on stage, and the night was a roaring success. Mozart ruled the evening with two scenes from The Magic Flute and a scene from The Marriage of Figaro. The singer-actors also presented scenes from Humperdink’s Hansel and Gretel and Verdi’s La Traviata. The highlight of the evening was an extended scene from Gian-Carlo Menotti’s The Consul. Not only did the singers extend themselves vocally learning the complexities of Menotti’s music, but they also incorporated the technique of singing an opera in concert style. In April 2009, DSU Opera students will present an original musical presentation, one which they are composing as part of their class. The 2008-09 school year saw the largest enrollment in The DSU Opera in twenty years. The students are growing consistently in their general knowledge of opera and in the intricacies of being an actor and singing performer.
Each new year brings an active spring semester to the Music Department at DSU. Almost immediately we have the privilege of hosting 120-150 high school instrumentalists through our annual Honor Band. This year 140 students played beautifully under the direction of guest conductor, Dr. Quincy Hilliard, from UL-Lafayette. As Dr. Hilliard is a native of Starkville, MS, it was nice to have someone with so many accomplishments return to his home state to work with these outstanding band members.
Adding to this outstanding faculty, I take great pleasure in announcing four new appointments to the DSU Department of Music while wishing Drs. Bradford, Meerdink, Waters, and Wojcik the best of luck in their new endeavors. New to our department are Mr. Josh Armstrong, Assistant Director of Bands and percussion instructor; Mr. Nicholaus Cummins, Director of Choral Activities; Mr. Joe Moore, Director of Bands; and Dr. Chad Payton, countertenor and Assistant Professor of Music. They are a very accomplished and congenial group of young men who will ably build on the successes of their talented predecessors. I also want to congratulate Dr. Kumiko Shimizu, faculty collaborative pianist, on her promotion to Associate Professor with tenure status. It is well-deserved.
The Chamber Singers and Chorale will present their Fall Concert on Sunday, October 15 at 3:00 p.m. at the Parish Center of Our Lady of Victories Catholic Church in Cleveland. To celebrate the 250th anniversary of Mozart’s birth, Chamber Singers will perform his Missa Brevis in C (“The Sparrow Mass”). Chorale will present a collection of pieces highlighting the special relationship between words and music, featuring the works of Johannes Brahms, David Childs, Gerald Finzi, and Steven Stucky, set to poetry by William Austin, Emily Dickinson, Walt Whitman, and others.
The audience enjoyed the well-mannered and well-prepared performances. One of the great things in this ensemble competition was that all participating ensembles included at least one freshman. It is often difficult for freshmen to experience public performances on stage at the early stage of their study at university. However, performing in an ensemble might have made the experience friendlier for them. All music faculty members available during the convocation judged the competition, giving valuable comments to the ensemble groups. The DSU Flute Trio and the Delta State Saxophone Quartet shared first place. After the competition, the participants spoke about their experiences in the preparation process, which was of benefit to their fellow students in the audience.
Since the production of Opera Originale in April 2009, the DSU Opera has been excited about their activities. Opera Originale was the result of original plays written by the students and set to music for the stage. These stories began with one set of words from which the students created different stories to be presented, each one highly unique. Dr. Mary Lenn Buchanan, Professor of Music and DSU Opera Director says, “Creative writing is an integral part of learning. It is a form of personal freedom, which allows the writer an outlet for expression of thoughts, feelings, and imaginations. Working in groups allowed them to collaborate with their peers to organize and develop a team idea. The result of this collaboration was three works titled The Golden Monkey, Bella, and To Each His Own.” The students involved in the writing collaboration project also sang the various roles in each work, costumed the characters, and staged their finished products.
Second, learn how to study and practice. Your instructors will suggest approaches to learning that they have found successful. Use them!! If you feel your time is not producing the desired result, don't hesitate to seek assistance from the faculty and staff. Finally, set priorities. If you can't get the very best grades in all of your subjects, then you must decide how best to distribute your efforts. Begin by recognizing that those who will later employ you are concerned with your abilities as a musician and, consequently you should give your maximum stress to your music commitments.
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The DSU Marching Band is a highly visible performing ensemble – participation at all rehearsals and performances is mandatory. Because of the inherent nature of ensemble classes, the grading system is different from other academic classes. Each member starts with a grade of “A”. If a member follows all prescribed guidelines and meets all expectation, he or she will receive a final grade of “A” for the course at the conclusion of the semester. However, if a member does not follow the guidelines and expectations as outlined in this handbook, he / she will have his / her final grade lowered. Grading is determined by your preparation, positive contribution, and attendance. Preparation refers to rehearsals and performances and includes, but it not limited to: music execution (may be demonstrated via a formal music check), drill execution, possession of appropriate and required materials (proper shoes, instrument, drill, music, flip folder, etc…), proper wearing of the uniform, instrument upkeep, and knowledge and adherence to DSU Band policies and procedures. A positive contribution to rehearsals and performances includes, but is not limited to: the demonstration of a positive attitude, strong work ethic, and team-oriented demeanor. Attendance includes both rehearsals and performances.
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The Bachelor of Music Education degree (B.M.E.) is designed to train and educate students to become teachers of music. Completing all requirements qualifies a candidate to apply for a license to teach in Mississippi. In addition to general education courses, music theory, music history, and performance, the curriculum includes courses that expose teacher candidates to human behavior, basic education principles, methods of instruction, as well as providing a chance to observe teachers in the field and gain experiencing teaching students. A candidate must be approved to enter the teacher education program by successfully completing lower level courses in music, by reaching upper level performance standards, and by exhibiting personal qualities that are deemed necessary for success as a teacher. Admission requirements to the degree program are initially based on an audition and music literacy exam. Candidates are expected have had prior musical experiences.
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One of my responsibilities as interim chair has been to attend the annual conference of the National Association of Schools of Music, or NASM, which awards accreditation to collegiate music units and music degrees. The meeting was held in Boston over the weekend before the Thanksgiving holidays. I was able to become more familiar with the process of institutional self-assessment and become more aware of the common issues among university music programs. Music departments and schools around the country all have financial concerns, look for new strategies to recruit and retain music majors, struggle with finding effective ways to meet changing educational expectations, and work to foster the success and competence of their students.
“This experience was a great opportunity to meet colleagues from around the country and hear about their schools and programs. Grading the exams for all those days was a great way to boost one’s skill level in a really short time!” Dr. Mary Lenn Buchanan, Professor of Music, was awarded a research grant through the DSU “Health And Wellness In The Delta” foundation fund. She attended the Care of The Professional Voice Symposium in Philadelphia, PA in May 2007 and is conducting informal research on health issues that affect the voice and classroom teachers. In association with the Delta Center for Culture and Learning, in June of 2007, Dr. Buchanan also presented a workshop to MS High School history teachers titled “The Legacy of Opera in Mississippi.” Dr. Buchanan continues to serve as director of the North MS District Metropolitan Opera auditions, secretary/treasurer for the MS National Association of Teachers of Singing conference, and chair of the Department of Music tenure and promotion committee.
The present paper proposed a new approach for detecting music boundaries in a music stream dataset. The proposed method extracts similar segment pairs in a music piece by segmental continuous dynamic programming and can identify the location of each music piece according to the information of occurrence positions of the similar segment pairs. The music boundaries are then determined. Experimental results reveal that the proposed approach is a promising method for detecting music boundaries between music pieces, while avoiding oversegmentation within music pieces. An optimal method for finding the acoustic changing points using GMM, and so on, will be studied in the future. Better parameter sets (feature vector, number of frame shift, etc.) must be investigated for this purpose. Evaluation should be performed using other music genres and real-world stream data, such as video data, because the experiments of the present study examined only the popular music genre and speech corpus data.
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