Effectiveness of HMM-Based Retrieval on Large Databases

We have investigated the performance of a hidden Markov model based QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical excerpts from various genres. This paper reports the performance of several variations of our retrieval system against different types of synthetic queries on the large database, where we can control the errors injected into the queries. We note several trends, among the most interesting is that as queries get longer (i.e., more notes) the retrieval performance improves

E-Bike Engagement and Accessibility

ME450 Capstone Design and Manufacturing Experience: Winter 2021There is currently a large barrier of entry to the design space surrounding a DIY E-bike retrofit build. The combination of necessary knowledge regarding gearing and transmission, mechatronics and motor control, battery charge and capacity, and force analyses, all in addition to general bike-related knowledge can be completely overwhelming to a novice user. Our team approached this project through a Socially-Engaged Design strategy to provide access, regardless of mechanical ability and budget, to a low-cost and easily accessible E-bike. We defined requirements for our solution design to incentivize the use, purchase or build of E-bikes, to be usable without much background knowledge, to be safe, to be attractive and accessible to the user, and to be inexpensive. We began our design process by conducting research on existing designs solutions. From here, we conveyed every step that a user would go through to access an E-bike themselves and how they could optimize an E-bike or E-bike retrofit of their own. Our theoretical stakeholder persona for this project is a University of Michigan student looking to commute across campus daily. This persona helped us build specifications such as hill climb ability, battery life and a target max flat ground speed. Primary subsystems were identified as system controls like throttling or pedal assist, motor and transmission, power supply, and the mounting interface. Our final CAD model of this design and its subsystems includes the motor, power supply, transmission, housing, and interfaces for any other subsystem. We also developed a model for a sustainable modular battery prototype design. This accomplishes our aim to make each battery cell replaceable while achieving a high cycle life compared to other batteries of similar size and cost. The transmission design solution provides a smooth ride up the steepest Ann Arbor hills and its mounting design provides easy access for maintenance and diagnosability. After completing the design, we evaluated it against our set specifications through physical testing and virtual analysis. We also examined the effectiveness of our design solution by evaluating the gap between existing market solutions and the user needs. Through our analysis, we realized that our retrofit design is a helpful tool to convey our DIY decision making process, though its mechanical complexity prevented it from properly addressing our defined need for increased E-bike accessibility. To effectively address the needs of our problem space, we determined that we should communicate our process to users through inclusive web design, rather than only conveying it through the prototype design. We therefore developed a website which takes the user through several pages covering our mission statement, E-bike related background information, evaluation criteria for E-Bike selection, the design and decision-making process, maintenance guides and safety practices, end-of-life recycling details, and opportunities for further customization of a DIY retrofit build. We validated our website solution against several inclusive web design and educational guidelines which include the Nielsen Norman group and the US Department of Education. While the website needs further building and revision for optimal accessibility, these verification techniques indicate that its framework and existing structure will help users access, regardless of mechanical ability and budget, to a low-cost and easily accessible E-bike. In the future, we plan to finalize a website design with the use of HTML and CSS programming and conduct usability tests with potential users to iterate on our design and improve it further.http://deepblue.lib.umich.edu/bitstream/2027.42/167638/1/Team_21-E-Bike_Engagement_and_Accessibility.pd

Effectiveness of hmm-based retrieval on large databases

We have investigated the performance of a hidden Markov model QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical excerpts from various genres. This paper reports the performance of several variations of our retrieval system against different types of synthetic queries on the large database, where we can control the errors injected into the queries. We note several trends, among the most interesting is that as queries get longer (i.e., mor

Name that tune: A pilot study in finding a melody from a sung query

We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. The sung theme (query) is converted into a sequence of pitch-intervals and rhythms. This sequence is compared to musical themes (targets) stored in a database. The top pieces are returned to the user in order of similarity to the sung theme. We describe, in detail, two different approaches to measuring similarity between database themes and the sung query. In the first, queries are compared to database themes using standard string-alignment algorithms. Here, similarity between target and query is determined by edit cost. In the second approach, pieces in the database are represented as hidden Markov models (HMMs). In this approach, the query is treated as an observation sequence and a target is judged similar to the query if its HMM has a high likelihood of generating the query. In this article we report our approach to the construction of a target database of themes, encoding, and transcription of user queries, and the results of preliminary experimentation with a set of sung queries. Our experiments show that while no approach is clearly superior to the other system, string matching has a slight advantage. Moreover, neither approach surpasses human performance

General Terms

We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observation sequence and a piece is judged similar to the query if its HMM has a high likelihood of generating the query. The top pieces are returned to the user in rank-order. This paper reports the basic approach for the construction of the target database of themes, encoding and transcription of user queries, and the results of initial experimentation with a small set of sung queries