Object-based Modeling of Audio for Coding and Source Separation

This thesis studies several data decomposition algorithms for obtaining an object-based representation of an audio signal. The estimation of the representation parameters are coupled with audio-specific criteria, such as the spectral redundancy, sparsity, perceptual relevance and spatial position of sounds. The objective is to obtain an audio signal representation that is composed of meaningful entities called audio objects that reflect the properties of real-world sound objects and events. The estimation of the object-based model is based on magnitude spectrogram redundancy using non-negative matrix factorization with extensions to multichannel and complex-valued data. The benefits of working with object-based audio representations over the conventional time-frequency bin-wise processing are studied. The two main applications of the object-based audio representations proposed in this thesis are spatial audio coding and sound source separation from multichannel microphone array recordings. In the proposed spatial audio coding algorithm, the audio objects are estimated from the multichannel magnitude spectrogram. The audio objects are used for recovering the content of each original channel from a single downmixed signal, using time-frequency filtering. The perceptual relevance of modeling the audio signal is considered in the estimation of the parameters of the object-based model, and the sparsity of the model is utilized in encoding its parameters. Additionally, a quantization of the model parameters is proposed that reflects the perceptual relevance of each quantized element. The proposed object-based spatial audio coding algorithm is evaluated via listening tests and comparing the overall perceptual quality to conventional time-frequency block-wise methods at the same bitrates. The proposed approach is found to produce comparable coding efficiency while providing additional functionality via the object-based coding domain representation, such as the blind separation of the mixture of sound sources in the encoded channels. For the sound source separation from multichannel audio recorded by a microphone array, a method combining an object-based magnitude model and spatial covariance matrix estimation is considered. A direction of arrival-based model for the spatial covariance matrices of the sound sources is proposed. Unlike the conventional approaches, the estimation of the parameters of the proposed spatial covariance matrix model ensures a spatially coherent solution for the spatial parameterization of the sound sources. The separation quality is measured with objective criteria and the proposed method is shown to improve over the state-of-the-art sound source separation methods, with recordings done using a small microphone array

Student Expectations: The effect of student background and experience

CONTEXT The perspectives and previous experiences that students bring to their programs of study can affect their approaches to study and the depth of learning that they achieve Prosser & Trigwell, 1999; Ramsden, 2003). Graduate outcomes assume the attainment of welldeveloped independent learning skills which can be transferred to the work-place. PURPOSE This 5-year longitudinal study investigates factors influencing students’ approaches to learning in the fields of Engineering, Software Engineering, and Computer Science, at two higher education institutes delivering programs of various levels in Australia and New Zealand. The study aims to track the development of student approaches to learning as they progress through their program. Through increased understanding of students’ approaches, faculty will be better able to design teaching and learning strategies to meet the needs of an increasingly diverse student body. This paper reports on the first stage of the project. APPROACH In August 2017, we ran a pilot of our survey using the Revised Study Process Questionnaire(Biggs, Kember, & Leung, 2001) and including some additional questions related to student demographics and motivation for undertaking their current program of study. Data were analysed to evaluate the usefulness of data collected and to understand the demographics of the student cohort. Over the period of the research, data will be collected using the questionnaire and through focus groups and interviews. RESULTS Participants provided a representative sample, and the data collected was reasonable, allowing the questionnaire design to be confirmed. CONCLUSIONS At this preliminary stage, the study has provided insight into the student demographics at both institutes and identified aspects of students’ modes of engagement with learning. Some areas for improvement of the questionnaire have been identified, which will be implemented for the main body of the study

A new strategy for active learning to maximise performance in intensive courses

This paper describes an innovation in the delivery of an introductory thermodynamics course offered to students studying towards an engineering qualification. The course was delivered in intensive format, across three weeks of study. Students find it challenging to engage with complex engineering topics in a short period of time, and there is no sizeable study break for pre-exam study. This means that students cannot afford to delay in learning and applying content. Every class must be an opportunity to interact with the content immediately. The innovation described here involved implementing a new daily structure for the course that attempted to mimic the standard process by which students learn material, apply it, study it and practice it in across a traditional-length semester. The new structure involved integrating the lecture and recitation components to the course to increasing the active learning during material delivery, then allowing students to engage in guided study and open-book formative assessment. This paper describes the implementation of this innovation. A brief review of the literature on intensive courses is provided, followed by a description of the approach used in this particular class. The results are then presented, and evaluated in the context of the research and the instructor’s own critical reflection

Chair a session/Integration of theory and practice in the learning and teaching process

The theme for AAEE-2017 is “Integrated Engineering”, which covers a range of sub-themes, such as: Integration of theory and practice in the learning and teaching process Interdisciplinary and cross-disciplinary engineering programs and learning environments Integration of teaching and research in the engineering training process The role and impact of engineering students and educators in the wider community Systems perspectives on engineering education. Integration is also about connections, e.g. between students and teachers, between students in learning together, and between educational institutions and industry and wider society in the engineering education process