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The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.