Thesis (B.A.)--Haverford College, Dept. of Economics, 2008. / Includes bibliographical references.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 90-92). Also available on the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 90-92). Also available on the Internet.

abstract: Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task. / Dissertation/Thesis / Ph.D. Electrical Engineering 2011

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Bing Benny; Committee Co-Chair: Fred B-H. Juang; Committee Member: Gee-Kung Chang. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Audio coding is used to compress digital audio signals, thereby reducing the amount of bits needed to transmit or to store an audio signal. This is useful when network bandwidth or storage capacity is very limited. Audio compression algorithms are based on an encoding and decoding process. In the encoding step, the uncompressed audio signal is transformed into a coded representation, thereby compressing the audio signal. Thereafter, the coded audio signal eventually needs to be restored (e.g. for playing back) through decoding of the coded audio signal. The decoder receives the bitstream and reconverts it into an uncompressed signal. ISO-MPEG is a standard for high-quality, low bit-rate video and audio coding. The audio part of the standard is composed by algorithms for high-quality low-bit-rate audio coding, i.e. algorithms that reduce the original bit-rate, while guaranteeing high quality of the audio signal. The audio coding algorithms consists of MPEG-1 (with three different layers), MPEG-2, MPEG-2 AAC, and MPEG-4. This work presents a study of the MPEG-4 AAC audio coding algorithm. Besides, it presents the implementation of the AAC algorithm on different platforms, and comparisons among implementations. The implementations are in C language, in Assembly of Intel Pentium, in C-language using DSP processor, and in HDL. Since each implementation has its own application niche, each one is valid as a final solution. Moreover, another purpose of this work is the comparison among these implementations, considering estimated costs, execution time, and advantages and disadvantages of each one.

Audio coding is used to compress digital audio signals, thereby reducing the amount of bits needed to transmit or to store an audio signal. This is useful when network bandwidth or storage capacity is very limited. Audio compression algorithms are based on an encoding and decoding process. In the encoding step, the uncompressed audio signal is transformed into a coded representation, thereby compressing the audio signal. Thereafter, the coded audio signal eventually needs to be restored (e.g. for playing back) through decoding of the coded audio signal. The decoder receives the bitstream and reconverts it into an uncompressed signal. ISO-MPEG is a standard for high-quality, low bit-rate video and audio coding. The audio part of the standard is composed by algorithms for high-quality low-bit-rate audio coding, i.e. algorithms that reduce the original bit-rate, while guaranteeing high quality of the audio signal. The audio coding algorithms consists of MPEG-1 (with three different layers), MPEG-2, MPEG-2 AAC, and MPEG-4. This work presents a study of the MPEG-4 AAC audio coding algorithm. Besides, it presents the implementation of the AAC algorithm on different platforms, and comparisons among implementations. The implementations are in C language, in Assembly of Intel Pentium, in C-language using DSP processor, and in HDL. Since each implementation has its own application niche, each one is valid as a final solution. Moreover, another purpose of this work is the comparison among these implementations, considering estimated costs, execution time, and advantages and disadvantages of each one.

Audio coding is used to compress digital audio signals, thereby reducing the amount of bits needed to transmit or to store an audio signal. This is useful when network bandwidth or storage capacity is very limited. Audio compression algorithms are based on an encoding and decoding process. In the encoding step, the uncompressed audio signal is transformed into a coded representation, thereby compressing the audio signal. Thereafter, the coded audio signal eventually needs to be restored (e.g. for playing back) through decoding of the coded audio signal. The decoder receives the bitstream and reconverts it into an uncompressed signal. ISO-MPEG is a standard for high-quality, low bit-rate video and audio coding. The audio part of the standard is composed by algorithms for high-quality low-bit-rate audio coding, i.e. algorithms that reduce the original bit-rate, while guaranteeing high quality of the audio signal. The audio coding algorithms consists of MPEG-1 (with three different layers), MPEG-2, MPEG-2 AAC, and MPEG-4. This work presents a study of the MPEG-4 AAC audio coding algorithm. Besides, it presents the implementation of the AAC algorithm on different platforms, and comparisons among implementations. The implementations are in C language, in Assembly of Intel Pentium, in C-language using DSP processor, and in HDL. Since each implementation has its own application niche, each one is valid as a final solution. Moreover, another purpose of this work is the comparison among these implementations, considering estimated costs, execution time, and advantages and disadvantages of each one.

With the constant advances in technology, people are using more wireless products, such as earphones or speakers whereas many of them use Bluetooth. With the current advances in Bluetooth technology, consumers and manufacturers have a hard time keeping up with the pace. Thus, when it comes to factors such as battery drainage, CPU usage, and range there is missing knowledge. This study is conducted to find out what effect the different codecs have on these factors, by comparing the two most commonly used codecs SBC and AAC. Using a codec that has lower battery drainage whilst still having a good enough audio quality can have a positive impact on our society and environment. Needing less electricity, lessens the overall energy consumption and directly lowers the energy production. Our results indicate that there is a significant difference in CPU usage but not in battery drainage or range.

L'évolution des systèmes de télécommunications conduit à la conception de codecs de la parole et du son de plus en plus sophistiqués, accroissant ainsi la concurrence de l'industrie de l'audio et accordant une importance grandissante à la qualité de service. Si l'évaluation de la qualité des codecs peut s'opérer suivant des mesures objectives ou subjectives, les secondes restent les plus fiables dans la mesure où la qualité perçue par les utilisateurs est intrinsèquement subjective. Toutefois, les tests subjectifs requièrent des signaux d'ancrage, i.e. des signaux artificiels visant la reproduction des défauts perceptifs des codecs de sorte que les dégradations provoquées soient aisément contrôlables. Le système de référence actuellement normalisé par l'Union Internationale des Télécommunications est le MNRU (Modulated Noise Reference Unit) qui simule le bruit de quantification introduit par les premiers codecs en forme d'onde. L'évolution de la technologie rend aujourd'hui ce système obsolète, et il s'agit donc de concevoir un nouveau système d'ancrage plus adapté aux codecs actuels. En considérant la qualité audio comme un objet multidimensionnel, nous avons mis en évidence un espace perceptif à quatre dimensions, et ce à partir de deux approches de réduction de dimensionnalité, l'AFM (Analyse Factorielle Multiple) et la MDS 3–voies (MultiDimensional Scaling). A partir des quatre dimensions identifiées – « Réduction de la largeur de bande », « Bruit de fond », « Écho/Réverbération » et « Distorsion de la parole » –, nous avons modélisé puis validé les signaux d'ancrage des trois premières dimensions et proposé deux modèles de signaux d'ancrage pour la quatrième. / The evolution of technology led to the design of very sophisticated speech and audio codecs. Accordingly, the competition in audio devices manufacturing has increased and today the quality of service becomes crucial for telecommunications operators. Quality of codecs is assessed through objective and subjective measures, the second ones being the most reliable since the quality perceived by users is inherently subjective. Nevertheless, subjective tests require anchor signals corresponding to artificial signals, which reproduce the perceptual impairments of codecs in such a manner that the amount of degradation can be easily controlled. The reference system currently standardized by the International Telecommunication Union is the Modulated Noise Reference Unit (MNRU), which simulates the quantization noise of the first generation of waveform codecs. Due to the evolution of codecs, the MNRU system became obsolete and researchers aim at designing a new reference system of anchor signals more suited to current codecs. Assuming that speech and audio quality is multidimensional, we first identified four perceptual dimensions using two dimensionality reduction techniques – the MFA (Multiple Factor Analysis) and the 3–way MDS (MultiDimensional Scaling). From the identified dimensions, namely “Bandwidth limitation”, “Background noise”, “Echo/Reverberation” and “Speech distortion”, we succeeded in modeling and validating anchor signals for three of them and we suggested two models of anchor signals for the last one.