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- All Subjects: Clarinet and piano music
Description
Despite the wealth of folk music traditions in Portugal and the importance of the clarinet in the music of bandas filarmonicas, it is uncommon to find works featuring the clarinet using Portuguese folk music elements. In the interest of expanding this type of repertoire, three new works were commissioned from three different composers. The resulting works are Seres Imaginarios 3 by Luis Cardoso; Delirio Barroco by Tiago Derrica; and Memória by Pedro Faria Gomes. In an effort to submit these new works for inclusion into mainstream performance literature, the author has recorded these works on compact disc. This document includes interview transcripts with each composer, providing first-person discussion of each composition, as well as detailed biographical information on each composer. To provide context, the author has included a brief discussion on Portuguese folk music, and in particular, the role that the clarinet plays in Portuguese folk music culture.
ContributorsFerreira, Wesley (Contributor) / Spring, Robert S (Thesis advisor) / Bailey, Wayne (Committee member) / Gardner, Joshua (Committee member) / Hill, Gary (Committee member) / Schuring, Martin (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsBurton, Charlotte (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-08
ContributorsDruesedow, Elizabeth (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
Description
This project includes a recording and performance guide for three newly commissioned pieces for the clarinet. The first piece, shimmer, was written by Grant Jahn and is for B-flat clarinet and electronics. The second piece, Paragon, is for B-flat clarinet and piano and was composed by Dr. Theresa Martin. The third and final piece, Duality in the Eye of a Bovine, was written by Kurt Mehlenbacher and is for B-flat clarinet, bass clarinet, and piano. In addition to the performance guide, this document also includes background information and program notes for the compositions, as well as composer biographical information, a list of other works featuring the clarinet by each composer, and transcripts of composer and performer interviews. This document is accompanied by a recording of the three pieces.
ContributorsPoupard, Caitlin Marie (Author) / Spring, Robert (Thesis advisor) / Gardner, Joshua (Thesis advisor) / Hill, Gary (Committee member) / Oldani, Robert (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance,
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
ContributorsArunachalam, Sairam (Author) / Chakrabarti, Chaitali (Thesis advisor) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2016
Description
The primary objective of this research project is to expand the clarinet repertoire with the addition of four new pieces. Each of these new pieces use contemporary clarinet techniques, including electronics, prerecorded sounds, multiphonics, circular breathing, multiple articulation, demi-clarinet, and the clari-flute. The repertoire composed includes Grant Jahn’s Duo for Two Clarinets, Reggie Berg’s Funkalicious for Clarinet and Piano, Rusty Banks’ Star Juice for Clarinet and Fixed Media, and Chris Malloy’s A Celestial Breath for Clarinet and Electronics. In addition to the musical commissions, this project also includes interviews with the composers indicating how they wrote these works and what their influences were, along with any information pertinent to the performer, professional recordings of each piece, as well as performance notes and suggestions.
ContributorsCase-Ruchala, Celeste Ann (Contributor) / Gardner, Joshua (Thesis advisor) / Spring, Robert (Thesis advisor) / Hill, Gary (Committee member) / Rogers, Rodney (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2016
ContributorsClements, Katrina (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-15
ContributorsClifton-Armenta, Tyler (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-16
ContributorsMoonitz, Olivia (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-13
ContributorsKierum, Caitlin (Contributor) / Novak, Gail (Pianist) (Performer) / Liang, Jack (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-11