Generating Music Medleys via Playing Music Puzzle Games

Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple non-overlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.Comment: Accepted at AAAI 201

Pop Music Highlighter: Marking the Emotion Keypoints

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.Comment: Transactions of the ISMIR vol. 1, no.

Practice Room Acoustics: What Matters to Musicians About the Practice Space

Why do people always prefer the practice room in the corner on the second floor than the others? What’s the reason why string players often go for the “dryer” room than the wind players? Wondering why brass players often occupy the resonant room? This paper is here to decipher all the mysteries behind all of the questions above by the acoustic analysis suggested by Bonnelo and the other supplying papers on sound absorbing materials. The question to be answered is how the rooms are different from each other in terms of their dimensions and damping surfaces. Eventually, construct a criteria for adjusting the acoustic characteristic by using sound absorbing materials. Through a survey filled out by conservatory students, it informs general preferences of choosing practice room and a frequency­ dependent reverberation time test is run accordingly. Having gathered all the information, it can be concluded that there is a direct relation between how preferable a room is and room dimension and the evenness of reverberation time throughout a frequency interval. Besides from the method of damping excessive resonance through locating high and low pressure area of the standing wave suggested by the Bonello paper, there are also possibilities of using different absorbing materials and Helmholtz resonator to change the reverberation time and modal density of the targeting frequency of a room. Therefore, with the results of this paper, musicians are a step closer to a simple yet career­ changing acoustic tool for constructing practice rooms

AUTISM AND SELF-DETERMINATION: MEASUREMENT AND CONTRAST WITH OTHER DISABILITY GROUPS

This dissertation consists of four chapters. Chapter 1 provides an introduction to the self-determination literature documenting the importance of promoting the self-determination of transition and secondary age students with disabilities, as well as a summary of research examining the self-determination of students with disabilities across disability categories, with a particular focus on students with autism spectrum disorders (ASD) and the need for additional research with this latter population. Chapter 2 investigates the factor structures of two instruments measuring the self-determination of students with ASD. Ninety-five middle and high school students (17% female and 83% male) ages 13 through 22 years participated in the investigation of the validity of two instruments, The Arc's Self-Determination Scale (SDS) and AIR Self-Determination Scale (AIR). A Confirmatory Factor Analysis (CFA) was conducted separately for the SDS and AIR data. The findings of this study indicated that the parameter estimates and the model fit results supported the hypothesized factor structure in this sample, at least for the first three of four factors of the SDS and fully supported the two factors of the AIR. Chapter 3 builds on the findings of Chapter 2 and examines the differences in self-determination among students with ASD, students with intellectual disability (ID), and students with learning disabilities (LD). A total of 222 participants with an equal size group for each of the three disability categories (ASD, ID, LD) were selected to participate in the comparison of total self-determination and domain scores. One-way between-subjects multivariate analysis of variance (MANOVA) was performed on six dependent variables/factors, including autonomy, self-regulation, psychological empowerment, self-realization, capacity, and opportunity. The results indicated that (a) students with ASD and ID and LD were different in their scores in these domains, and (b) students with ASD had lower levels of autonomy when compared to students with LD. Chapter 4 presents the conclusions and implications of the findings of Chapter 2 and 3. The primary implications for future research indicate that the factors of the two self-determination measures can be used as reliable outcome variables useful for detecting treatment effects of experimental design studies promoting the self-determination of students with ASD. Also, future research is encouraged to investigate the items that loaded negatively onto Self-Realization domain of the SDS. In addition to significant group differences in self-determination among three disability groups, future research should examine group differences in each essential characteristic of self-determination or in the component elements of self-determined behavior to provide a more completed profile of relative self-determination for this group. The primary implications for educators were that the two commonly used instruments are applicable to the population of students with ASD. Also, students with ASD, ID, and LD need instruction to promote self-determination, but students with ASD also need instructional emphases on several component elements as shown by the domain-level differences found in this study