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Full-Text Articles in Signal Processing
Quantized Nonnegative Matrix Factorization, Ruairí De Fréin
Quantized Nonnegative Matrix Factorization, Ruairí De Fréin
Conference papers
Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF’s (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in …
Adaptive Translinear Analog Signal Processing: A Prospectus, Eric Mcdonald, Kofi Odame, Bradley Minch
Adaptive Translinear Analog Signal Processing: A Prospectus, Eric Mcdonald, Kofi Odame, Bradley Minch
Bradley Minch
We have devised a systematic method of transforming high-level time-domain descriptions of linear and nonlinear adaptive signal-processing algorithms into compact, continuous-time analog circuitry using basic units called multiple-input translinear elements (MITEs). In this paper, we describe the current state of the art and illustrate the method with an example of an analog phase-locked loop (PLL).
Application Of Speech Recognition To African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson
Application Of Speech Recognition To African Elephant (Loxodonta Africana) Vocalizations, Patrick J. Clemins, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
This paper presents a novel application of speech processing research, classification of African elephant vocalizations. Speaker identification and call classification experiments are performed on data collected from captive African elephants in a naturalistic environment. The features used for classification are 12 mel-frequency cepstral coefficients plus log energy computed using a shifted filter bank to emphasize the infrasound range of the frequency spectrum used by African elephants. Initial classification accuracies of 83.8% for call classification and 88.1% for speaker identification were obtained. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal …