Масштабируемые аудиоречевые кодеры на основе адаптивного частотно-временного анализа звуковых сигналов

In the paper is discussed the methods of perceptual sub-band audio signal processing with the dynamic time-frequency map transformation based on the discrete wavelet packet (WP) transform. The advantages of it is that the growing process of WP tree is going from the top to down without returning to smaller scale levels of decomposition and needing to build a complete WP tree, that corresponds to the concept of scalable audio/speech coders implementation in real time. The objective quality assessment of proposed coders based techniques PEMO-Q and comparing with the widespread encoders Opus and Vorbis are given. It shows that the reconstructed signal complies with ITU-R PEAQ at a high compression ratio up to 18 times or more, does not contain artifacts and noise to mask ration less -9 dB.В статье рассматриваются методы перцептуальной субполосной обработки звуковых сигналов с динамической трансформацией частотно-временного плана на основе пакетного дискретного вейвлет-преобразования (ПДВП), достоинством которых является то, что рост дерева осуществляется сверху вниз, без возвратов на меньшие масштабные уровни преобразования и необходимости построения полного дерева ПДВП, что соответствует концепции реализации масштабируемых аудиоречевых кодеров в реальном масштабе времени. Приводятся объективные оценки качества предлагаемых кодеров на основе методики PEMO-Q и сравнения с широко распространенными кодерами Opus и Vorbis, которые показывают, что реконструированный сигнал соответствует требованиям стандарта ITU-R PEAQ при высокой степени компрессии в 18 и более раз, не содержит артефактов: отношение мощности шума к порогу маскирования 〖NMR〗_total меньше –9 дБ

An analysis of psychoacoustically-inspired matching pursuit decompositions of speech signals

International audienceMatching pursuit (MP), particularly using the Gammatones dictionary , has become a popular tool in sparse representations of speech/audio signals. The classical MP algorithm does not however take into account psychoacoustical aspects of the auditory system. Recently two algorithms, called PAMP and PMP have been introduced in order to select only perceptually relevant atoms during MP decomposition. In this paper we compare this two algorithms on few speech sentences. The results suggest that PMP, which also has the strong advantage of including an implicit stop criterion, always outperforms PAMP as well as classical MP. We then raise the question of whether the Gam-matones dictionary is the best choice when using PMP. We thus compare it to the popular Gabor and damped-Sinusoids dictionaries. The results suggest that Gammatones always outperform damped-Sinusoids, and that Gabor yield better reconstruction quality but with higher atoms rate

Analysis, Visualization, and Transformation of Audio Signals Using Dictionary-based Methods

date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +0000date-added: 2014-01-07 09:15:58 +0000 date-modified: 2014-01-07 09:15:58 +000

Internship report

National audienceMatching pursuit (MP), particularly using the Gammatones dic- tionary, has become a popular tool in sparse representations of speech/audio signals. The classical MP algorithm does not however take into account psychoacoustical aspects of the audi- tory system. Recently two algorithms, called PAMP and PMP have been introduced in order to select only perceptually rele- vant atoms during MP decomposition. In this paper we compare this two algorithms on few speech sentences. The results sug- gest that PMP, which also has the strong advantage of including an implicit stop criterion, always outperforms PAMP as well as classical MP. We then raise the question of whether the Gam- matones dictionary is the best choice when using PMP. We thus compare it to the popular Gabor and damped-Sinusoids dictio- naries. The results suggest that Gammatones always outperform damped-Sinusoids, and that Gabor yield better reconstruction quality but with higher atoms rate