A system for speaker tracking in broadcast-news audio data is presented and the impacts of the main components of the system to the overall speaker-tracking performance are evaluated. The process of speaker tracking in continuous audio streams involves several processing tasks and is therefore treated as a multistage process. The main building blocks of such system include the components for au...

A typical content-based audio management system deals with three aspects namely audio segmentation and classification, audio analysis, and content-based retrieval of audio. In this paper, we integrate the three aspects of content-based audio management into a single framework and propose an efficient method for flexible querying and browsing of auditory data. More specifically, we utilize two r...

Searching audio streams is currently a tedious task. There exist no tools that allow a user to quickly scan through a long video lecture or audio book using only audio cues. In this paper I propose and build a simple system that alleviates the burden of searching through a large amount of audio data. This system use concepts from other text and speech summarization work, but focuses on the prob...

Performance of conventional text based audio search engines can be improved with feature based search engines. In this paper, text independent audio ranking system for audio engines with audio signal as query is proposed. Discrete Wavelet Transform (DWT) is used for feature extraction. Ranking is obtained using three different distance metrics namely Euclidean distance, Manhattan distance and M...

In this paper we present a system for audio-visual speech recognition based on a hybrid Artificial Neural Network/Hidden Markov Model (ANN/HMM) approach. To setup the system it was necessary to record a new audio-visual database. We will describe the recording and labeling of the database. The fusion of audio and video data is a key aspect of the paper. Three conditions, when only the audio or ...

In this paper we present a system for audio-visual speech recognition based on a hybrid Artificial Neural Network/Hidden Markov Model (ANN/HMM) approach. To setup the system it was necessary to record a new audio-visual database. We will describe the recording and labeling of the database. The fusion of audio and video data is a key aspect of the paper. Three conditions, when only the audio or ...

Automatic audio content analysis is a general research area in which algorithms are developed to allow computer systems to understand the content of digital audio signals for further exploitations. The main focus therein is on the practical applications for audio files management, like automatic labeling, efficient browsing, or the retrieval of relevant files with little effort from a big datab...

Soundscape composition is the creative practice of processing and combining sound recordings to evoke auditory associations and memories within a listener. We present Audio Metaphor, a system for creating novel soundscape compositions. Audio Metaphor processes natural language queries derived from Twitter for retrieving semantically linked sound recordings from online user-contributed audio dat...

In this thesis, I designed a system for augmenting a space with linked audio. Using this system, individuals can associate audio clips with a location in real-world space. When an individual using the system passes through this augmented space, he or she can hear the audio clips that have been left by traveling through the associated locations. Furthermore, audio clips in the environment can be...

With an optimal network topology and tuning of hyperparameters, artificial neural networks (ANNs) may be trained to learn a mapping from low level audio features to one or more higher-level representations. Such artificial neural networks are commonly used in classification and regression settings to perform arbitrary tasks. In this work we suggest repurposing autoencoding neural networks as mu...