Uma análise do método cepstral de cancelamento de realimentação acústica

Esse trabalho analisa o método cepstral de cance- lamento de realimentação acústica. Demonstra-se que o erro do filtro adaptativo é composto pela soma de dois termos. O primeiro é relacionado somente ao caminho de realimentação e tende a zero, convergindo mais rapidamente para valores menores do fator de esquecimento. O segundo é relacionado aos cepstros do sinal de entrada e ao ganho do sistema de sonorização, contendo uma soma ponderada dos cepstros em que as ponderações são potências do fator de esquecimento. Simulações mostraram que, para sinais de fala, a soma ponderada dos cepstros converge, fato preponderante para a convergência do método. A velocidade de convergência, o valor após a convergência e as oscilações ao redor desse valor do segundo termo diminuem com o aumento do fator de esquecimento, comportamentos diretamente refletidos no erro do filtro. Nas primeiras iterações, o primeiro termo tem maior influência. Após algumas iterações, o segundo termo rege o desempenho do método.This work analyzes the cepstral method for acoustic feedback cancellation. It is shown that the adaptive filter error is composed of the sum of two terms. The first is related only to the feedback path and tends to zero, converging more quickly to smaller values of the forgetting factor. The second is related to the input signal cepstra and the gain of the reinforcement system, containing a weighted sum of the cepstra where the weights are powers of the forgetting factor. Simulations showed that, for speech signals, the weighted sum of the cepstra converges, a preponderant fact for the method convergence. The convergence speed, the value after the convergence and the oscillations around this value of the second term decrease with the increase of the forgetting factor, behaviors directly reflected in the filter error. In the first iterations, the first term has the greatest influence. After a few iterations, the second term governs the method performance.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Editorial: advances in machine learning approaches and technologies for supporting nervous system disease diagnosis

info:eu-repo/semantics/publishedVersio