Thesis (Ed.M.)--Boston University

Thesis (M.M.)--Boston University

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This dissertation seeks to identify the purposive force that determines the form and function of commercial stereophony in capitalist society, and the ways in which this force affects the productive and consumptive activities of stereophonic practitioners and listening audiences. Employing dialectical materialism, I examine three social processes that either historically established or continue to influence the mediative potential of stereophonic sound: the invention and industrial standardization of the stereophonic apparatus, the professionalization of stereophonic practitioners, and the social construction of stereophonic listeners as a mass consuming audience. These interrelated studies reveal perceived economic necessity as the dominant causal force that governs all stereophonic processes and practices under the capitalist economic system. Informed by my chapter findings, which complicate Karl Marx’s materialist base and superstructure schema – a coarse conceptual abstraction of capitalist production, I construct a more refined and flexible schematic diagram that offers a distinctive bird’s eye view of the universal interplay between capitalists, producers and consumers. This novel conceptual schematic depicts productive forces and productive relations as coterminous expressions of the dual-purpose of capitalism: to produce surplus-value for accumulation by capitalists, and to do so in perpetuity.

Adolescents with behavioral difficulties and emotional and behavior disorders (EBD) or other health impairment (OHI) have demonstrated deficits in reading, and these deficits appear to remain stable or worsen over time. Reading fluency is an essential skill for overall reading achievement, yet relatively few studies have addressed reading fluency intervention for adolescents, particularly adolescents with behavioral difficulties. This study used a multiple baseline across participants design to evaluate the effect of a repeated reading intervention on the reading fluency and comprehension skills of middle school students with reading difficulties and behavioral difficulties and EBD or OHI. The intervention involved repeated reading combined with an audio-recorded model and cues to read for comprehension. Working independently at a classroom computer, participants received six to nine minutes of daily supplemental fluency instruction over a four-week period. Instruction involved listening to an audio recording of a model reading a passage, receiving cues to read for understanding, reading the passage aloud while using the computer to record the reading, listening to the recording, and reading the passage aloud again while recording. Results indicated no functional relation between the intervention and the number of words correct per minute or the percentage of comprehension questions answered correctly. However, on-task behavior did improve during study session when compared with on-task behavior during regular classroom instruction. The findings of the study have implications for addressing the needs of adolescents with behavioral difficulties who have reading difficulties.

Higher education in the United States has a long history. Until the mid-1970's, higher education was primarily a not-for-profit industry. The rise of for-profit educational institutions (FPEIs) in the United States has created competition for traditional not-for-profit educational institutions (NFPEIs). As the growth of FPEIs continues, educational organizations are adopting businesslike approaches and quality measures to retain students. Measuring student satisfaction is one method for determining service quality, and can provide valuable insight into the students' expectations and perceived service quality. The recent introduction of an FPEI's audio production program in the Central Texas region has created competition for an already established NFPEI. In an attempt to find out whether expectation, satisfaction, or service gap differences exist between the programs, student satisfaction feedback was solicited from audio production enrollees at both institutions via the Noel-Levitz® Student Satisfaction InventoryTM. The instrument was used as a comparative tool at the program level and included 10 additional audio-production items. To aid in understanding how the local programs compare to one another, results were compared between the programs as opposed to using the national institutional-level benchmarks provided by Noel-Levitz®. Results indicate similar expectations in terms of priorities, but students at the FPEI have higher expectation levels overall. The satisfaction priorities had similarities as well, but students at the FPEI reported lower satisfaction in general. Finally, service gap measures indicated greater service gaps at the FPEI than at the NFPEI.

No description available.

When recording voice samples from a patient in speech therapy the quality of the recording may be affected by different signal corruptions, for example background noise or clipping. The equipment and expertise required to identify small disturbances are not always present at smaller clinics. Therefore, this study investigates possible machine learning algorithms to automatically detect selected corruptions in speech signals, including infrasound and random muting. Five algorithms are analyzed: kernel substitution based Support Vector Machine, Convolutional Neural Network, Long Short-term Memory (LSTM), Gaussian Mixture Model based Hidden Markov Model and Generative Model based Hidden Markov Model. A tool to generate datasets of corrupted recordings is developed to test the algorithms in both single-label and multi-label settings. Mel-frequency Cepstral Coefficients are used as the main features. For each type of corruption different ways to increase the classification accuracy are tested, for example by using a Voice Activity Detector to filter out less relevant parts of the recording, changing the feature parameters, or using an ensemble of classifiers. The experiments show that a machine learning approach is feasible for this problem as a balanced accuracy of at least 75% is reached on all tested corruptions. While the single-label study gave mixed results with no algorithm clearly outperforming the others, in the multi-label case the LSTM in general performs better than other algorithms. Notably it achieves over 95% balanced accuracy on both white noise and infrasound. As the algorithms are trained only on spoken English phrases the usability of this tool in its current state is limited, but the experiments are easily expanded upon with other types of audio recordings, corruptions, features, or classification algorithms. / När en patients röst spelas in för analys i talterapi kan inspelningskvaliteten påverkas av olika signalproblem, till exempel bakgrundsljud eller klippning. Utrustningen och expertisen som behövs för att upptäcka små störningar finns dock inte alltid tillgänglig på mindre kliniker. Därför undersöker denna studie olika maskininlärningsalgoritmer för att automatiskt kunna upptäcka utvalda problem i talinspelningar, bland andra infraljud och slumpmässig utsläckning av signalen. Fem algoritmer analyseras: stödvektormaskin, Convolutional Neural Network, Long Short-term Memory (LSTM), Gaussian mixture model-baserad dold Markovmodell och generatorbaserad dold Markovmodell. Ett verktyg för att skapa datamängder med försämrade inspelningar utvecklas för att kunna testa algoritmerna. Vi undersöker separat fallen där inspelningarna tillåts ha en eller flera problem samtidigt, och använder framförallt en slags kepstralkoefficienter, MFCC:er, som särdrag. För varje typ av problem undersöker vi också sätt att förbättra noggrannheten, till exempel genom att filtrera bort irrelevanta delar av signalen med hjälp av en röstupptäckare, ändra särdragsparametrarna, eller genom att använda en ensemble av klassificerare. Experimenten visar att maskininlärning är ett rimligt tillvägagångssätt för detta problem då den balanserade träffsäkerheten överskrider 75%för samtliga testade störningar. Den delen av studien som fokuserade på enproblemsinspelningar gav inga resultat som tydde på att en algoritm var klart bättre än de andra, men i flerproblemsfallet överträffade LSTM:en generellt övriga algoritmer. Värt att notera är att den nådde över 95 % balanserad träffsäkerhet på både vitt brus och infraljud. Eftersom algoritmerna enbart tränats på engelskspråkiga, talade meningar så har detta verktyg i nuläget begränsad praktisk användbarhet. Däremot är det lätt att utöka dessa experiment med andra typer av inspelningar, signalproblem, särdrag eller algoritmer.

To advance social work practice and decrease the research practice gap, this dissertation followed a model development paradigm consisting of several phases. Based on the task-centered model of social work practice and motivational interviewing, a new combined model was construed. The two underlying models were analyzed and synthesized, using technical eclecticism as the integrative approach. The resulting combined model was described by guidelines and manualized. To test the combined model in an applied setting, a study was designed in collaboration with social workers at a substance abuse counseling center. There, the combined model intervention was implemented in an outpatient medication assisted treatment program dispensing methadone and buprenorphine to a mainly African-American population. The agency program aimed at detoxification, but also provided methadone maintenance. It offered additional groups and acupuncture. Ten clients and four social work practitioners participated in the intervention study. The study used a mixed-method approach in data collection and analysis. Client practitioner verbal interaction was recorded using digital audio recording. The digital audio files were loaded directly into Atlas.ti software to be used for analysis. Qualitative data analysis with Atlas.ti was performed for two research tasks, a) assessing implementation fidelity of the manual based intervention and b) exploring model development aspects to improve model guidelines. Treatment fidelity was analyzed through deductive coding and frequency counts. Model development analysis was performed similar to a grounded theory model and used content analysis and constant comparison methodologies. Addiction Severity Index and Readiness Ruler, urine drug screens, problem change, and task accomplishment ratings were used as quantitative outcome measures to produce time series data in order to chart individual case progress in a single system design. After testing the intervention, a focus group with participating practitioners was conducted. Overall beneficence with clients improving and progressing successfully in the program was found. The integration of the underlying models was deemed successful. Their elements were found to be complementary and intricately linked. Crucial for successful implementation is that the program environment supports and accepts client choices. Model guidelines were reviewed and improved for further field testing.

Several available free software audio solutions were analyzed, and Jackdmp—a C++ reimplementation of the renowned JACK Audio Connection Kit—was selected as the most appropriate solution for a real-time audio architecture on DROPS. The JACK sound architecture provides the lowest processing latency possible on a desktop computer for a given set of sound card parameters. It reduces the latency jitter caused by software to zero and synchronizes streams at sample accuracy. A real-time admission scheme for JACK clients is proposed. The execution time of different typical JACK clients was analyzed with measurements to validate the assumptions the proposal is based on, but also to gain further knowledge about their timing behavior. The measurements showed that the condition set by Paul Davis—the time to process a client must be a linear function of the buffer size—holds for all tested clients. Jackdmp was ported to DROPS. The developed design of the port and its implementation is documented here. Measurements showed that—although the real-time performance of the Linux kernel is continuously being improved in the mainline and on special external branches—DROPS can provide a signaling latency that is two times lower on average than the values that can be achieved on the same machine running with a low latency patched Linux kernel. Thus, it can be stated that DROPS is well-suited for real-time audio processing and that the pursued path to use it as the foundation of a truly real-time capable audio workstation should be followed. / Wenn man heute digitale Audiotechnik zum Aufnehmen oder Abmischen von Musik oder anderen Audiodaten verwenden möchte, steht man vor der Wahl, entweder auf eine sehr spezialisierte Hardwarelösung zurückzugreifen oder aber sich eines gewöhnlichen Desktopsystems mit entsprechender Audiosoftware zu bedienen. Der Vorteil eines Desktopsystems ist neben seinem deutlich niedrigeren Preis vor allem die Flexibilität. Bezüglich seines Echtzeitverhaltens bietet ein Computer mit einem Standard-Desktop-Betriebssystem aber bei weitem nicht dieselbe Verlässlichkeit einer spezialisierten Hardwarelösung oder analoger Technik. Die Architektur von DROPS --- mit dem echtzeitfähigen Fiasco- Mikrokern auf der einen Seite sowie der Unterstützung von Legacy-Anwendungen durch L4Linux auf der anderen Seite --- birgt die Hoffnung, die Vorteile von den beiden eben beschriebenen Welten auf einem System mit DROPS kombinieren zu können. Die Motivation meiner Arbeit war es, für dieses langfristige Ziel einen ersten Grundstein zu legen. Dazu war es meine Aufgabe, verschiedene Open-Source- Lösungen hinsichtlich ihrer Eignung als Echtzeit-Audioarchitektur für DROPS zu analysieren und die am besten geeignete auf L4Env zu portieren. Meine Wahl fiel dabei auf das in der Linux-Audio-Szene wohlbekannte Jack Audio Connection Kit (JACK). Desweiteren konnte ich in der Arbeit untersuchen, wie sich die JACK Audioarchitektur in ein globales Echtzeit-Scheduling --- z.B. eines von DROPS --- einbetten ließe, und schlage eine generische Methode dafür vor.