Topic
Noise measurement
About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.
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TL;DR: In this article, a first microphone at the position close to a speaker and a signal including remaining noise is outputted from the first microphone, followed by a noise elimination circuit subtracting the estimated background noise from the received voice signal to supply the resultant signal to the speaker.
Abstract: A telephone set is provided with a first microphone at the position close to a speaker and a signal including remaining noise is outputted from the first microphone. Background noise is outputted from a second microphone. A noise elimination circuit estimates background noise reaching the ear of a receiver based on the background noise signal, the signal including the remaining noise, and a received voice signal from a transmitter. Also, the noise elimination circuit subtracts the estimated background noise from the received voice signal to supply the resultant signal to the speaker.
63 citations
TL;DR: In this paper, a dense convolutional network (DCN) with self-attention was proposed for speech enhancement in the time domain. But the proposed loss is based on magnitudes only, a constraint imposed by noise prediction ensures that the loss enhances both magnitude and phase.
Abstract: Speech enhancement in the time domain is becoming increasingly popular in recent years, due to its capability to jointly enhance both the magnitude and the phase of speech. In this work, we propose a dense convolutional network (DCN) with self-attention for speech enhancement in the time domain. DCN is an encoder and decoder based architecture with skip connections. Each layer in the encoder and the decoder comprises a dense block and an attention module. Dense blocks and attention modules help in feature extraction using a combination of feature reuse, increased network depth, and maximum context aggregation. Furthermore, we reveal previously unknown problems with a loss based on the spectral magnitude of enhanced speech. To alleviate these problems, we propose a novel loss based on magnitudes of enhanced speech and a predicted noise. Even though the proposed loss is based on magnitudes only, a constraint imposed by noise prediction ensures that the loss enhances both magnitude and phase. Experimental results demonstrate that DCN trained with the proposed loss substantially outperforms other state-of-the-art approaches to causal and non-causal speech enhancement.
63 citations
TL;DR: In this article, the authors investigated the avalanche multiplication and excess noise properties of a range of submicron Si diodes and found that the excess noise was below that predicted by conventional local noise theory.
Abstract: The avalanche multiplication and excess noise properties of a range of submicron Si diodes have been investigated. In these thin diodes the excess noise is found to fall below that predicted by conventional local noise theory. Modeling of the multiplication and excess noise using a recurrence method, which includes the dead space for carrier ionization, gives good agreement with experiment. This suggests that the dead space can reduce the excess noise in submicron Si diodes.
63 citations
15 Jun 2008
TL;DR: In this paper, a monolithic millimeter-wave integrated circuit (MMIC) amplifier with a noise temperature of NT=390 K (NF=3.7 dB) was presented.
Abstract: Measurement of the humidity profile of the atmosphere is highly important for atmospheric science and weather forecasting. This sounding measurement is obtained at frequencies close to the resonance frequency of water molecules (183 GHz). We have designed and characterized a MMIC low noise amplifier that will increase the sensitivity of sounding instruments at these frequencies. This study demonstrated a factor of two improvement in MMIC LNA noise temperature at this frequency band. The measured packaged InP monolithic millimeter-wave integrated circuit (MMIC) amplifier had a noise temperature of NT=390 K (NF=3.7 dB). The circuit was fabricated in 35 nm InP high electron mobility transistor (HEMT) process.
62 citations
TL;DR: In this paper, the authors proposed a method to remove coherent noise from seismic data by first making an approximate model of the noise and then adaptively matching the modeled noise to the data.
Abstract: Coherent noise may be removed from seismic data by first making an approximate model of the noise, then producing an even better estimation of the noise by adaptively matching the modeled noise to the data. This modified model of the noise may then be subtracted from the data, eliminating most coherent noise. The success of this approach depends both on how well the initial model matches the true noise and the success of the adaptive matching in modifying the initial noise prediction to match the true noise. The adaptive matching step is complicated by the presence of the signal and other noise in the data. In this article, the noise of interest is surface-related multiples, although other types of coherent noise may be removed with this approach.
62 citations