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Description
Advances in computational processing have made big data analysis in fields like music information retrieval (MIR) possible. Through MIR techniques researchers have been able to study information on a song, its musical parameters, the metadata generated by the song's listeners, and contextual data regarding the artists and listeners (Schedl, 2014). MIR research techniques have been applied within the field of music and emotions research to help analyze the correlative properties between the music information and the emotional output. By pairing methods within music and emotions research with the analysis of the musical features extracted through MIR, researchers have developed predictive models for emotions within a musical piece. This research has increased our understanding of the correlative properties of certain musical features like pitch, timbre, rhythm, dynamics, mel frequency cepstral coefficients (MFCC's), and others, to the emotions evoked by music (Lartillot 2008; Schedl 2014) This understanding of the correlative properties has enabled researchers to generate predictive models of emotion within music based on listeners' emotional response to it. However, robust models that account for a user's individualized emotional experience and the semantic nuances of emotional categorization have eluded the research community (London, 2001). To address these two main issues, more advanced analytical methods have been employed. In this article we will look at two of these more advanced analytical methods, machine learning algorithms and deep learning techniques, and discuss the effect that they have had on music and emotions research (Murthy, 2018). Current trends within MIR research, the application of support vector machines and neural networks, will also be assessed to explain how these methods help to address the two main issues within music and emotion research. Finally, future research within the field of machine and deep learning will be postulated to show how individuate models may be developed from a user or a pool of user's listening libraries. Also how developments of semi-supervised classification models that assess categorization by cluster instead of by nominal data, may be helpful in addressing the nuances of emotional categorization.
ContributorsMcgeehon, Timothy Makoto (Author) / Middleton, James (Thesis director) / Knowles, Kristina (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This experiment investigated the effects of different vortex generator sizes and configurations on the induced drag of a 2006 Honda Accord, with comparisons to a control test. Tuft tests were carried out prior to any data collection. The tufts were placed along the roof and rear window of the vehicle for each of the five vortex generator types. Video was taken of the tufts for each set of vortex generators, allowing a visual comparison of the flow characteristics with comparison to the control. Out of the four vortex generators tested, the two that yielded the most substantial change in the flow characteristics were utilized. The data collection was conducted utilizing the two sets of vortex generators, one large and one small, placed in three different locations along the roof of the vehicle. Over a course of four trials of data collection, each vortex generator size and configuration was tested two times along a stretch of Interstate 60, with each data set consisting of five minutes heading east, followed by five minutes heading west. Several experimental parameters were collected using an OBD II Bluetooth Adaptor, which were logged using the software compatible with the adaptor. This data was parsed and analyzed in Microsoft Excel and MATLAB. Utilizing an Analysis of Variance (ANOVA) analytical scheme, the data was generalized to account for terrain changes, steady state speed fluctuations, and weather changes per night. Overall, upon analysis of the data, the vortex generators showed little-to-no benefit to either the fuel efficiency or engine load experienced by the vehicle during the duration of the experiment. This result, while unexpected, is substantial as it shows that the expenditure of purchasing these vortex generators for this make and model of vehicle, and potentially other similar vehicles, is unnecessary as it produces no meaningful benefit.
ContributorsMazza, Seth (Author) / Walther, Chase (Co-author) / Takahashi, Timothy (Thesis director) / Middleton, James (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05
Description
This experiment investigated the effects of different vortex generator sizes and configurations on the induced drag of a 2006 Honda Accord, with comparisons to a control test. Tuft tests were carried out prior to any data collection. The tufts were placed along the roof and rear window of the vehicle for each of the five vortex generator types. Video was taken of the tufts for each set of vortex generators, allowing a visual comparison of the flow characteristics with comparison to the control. Out of the four vortex generators tested, the two that yielded the most substantial change in the flow characteristics were utilized. The data collection was conducted utilizing the two sets of vortex generators, one large and one small, placed in three different locations along the roof of the vehicle. Over a course of four trials of data collection, each vortex generator size and configuration was tested two times along a stretch of Interstate 60, with each data set consisting of five minutes heading east, followed by five minutes heading west. Several experimental parameters were collected using an OBD II Bluetooth Adaptor, which were logged using the software compatible with the adaptor. This data was parsed and analyzed in Microsoft Excel and MATLAB. Utilizing an Analysis of Variance (ANOVA) analytical scheme, the data was generalized to account for terrain changes, steady state speed fluctuations, and weather changes per night. Overall, upon analysis of the data, the vortex generators showed little-to-no benefit to either the fuel efficiency or engine load experienced by the vehicle during the duration of the experiment. This result, while unexpected, is substantial as it shows that the expenditure of purchasing these vortex generators for this make and model of vehicle, and potentially other similar vehicles, is unnecessary as it produces no meaningful benefit.
ContributorsWalther, Chase (Author) / Mazza, Seth (Co-author) / Takahashi, Timothy (Thesis director) / Middleton, James (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05