Simultaneous Measurement of Impulse Response and Distortion with a Swept-Sine Technique

By Angelo Farina. 2000

1,108 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aeaba8bb33cad2f6914cc02c76c52064d58a8136

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Citations in the corpus

Impulse response estimation for robust speech recognition in a reverberant environment (2012, methodology): “The ESS technique, introduced by Farina [3], is based on an exponential time-growing frequency sweep, as described by the following relationship: x(t) = sin  ω1 · L ln ( ω2 ω1 ) (e t L ·ln(ω2 ω1 ) − 1)  (2)”
Advancements in Impulse Response Measurements by Sine Sweeps (2007, background): “Sine sweeps are employed since long time for audio and acoustics measurements, but in recent years (2000 and later) their usage became much larger, thanks to the computational capabilities of modern computers.”
The PASCAL CHiME speech separation and recognition challenge (2013, methodology): “The measurements were made using Farina’s sine sweep method (Farina, 2000).”
Contaminated speech training methods for robust DNN-HMM distant speech recognition (2017, methodology): “The method adopted here for estimating IR consists in diffusing in the target environment a known Exponential Sine Sweep (ESS) signal, and recording it by a microphone properly placed in space [28].”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, methodology): “Several techniques have been proposed in the last decade for measuring the IR of an acoustic enclosure, including solutions based on Maximum Length Sequence (MLS) [36], Linear Chirps [37], or Exponential Sine Sweeps (ESS) [37].”
On the selection of the impulse responses for distant-speech recognition based on contaminated speech training (2014, methodology): “In particular, the latter work shows that the Exponential Sine Sweep (ESS) method proposed by Farina [13] signiﬁcantly outperforms other methods such as Linear Sine Sweep (LSS) and Maximum Length Sequence (MLS) thanks to a better processing of the harmonic distortion generated by the loudspeaker.”
The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments (2015, methodology): “The method adopted to estimate an IR consists in diffusing a known Exponential Sine Sweep (ESS) signal in the target environment, and recording it by the available microphones [29].”
Database of Multichannel In-Ear and Behind-the-Ear Head-Related and Binaural Room Impulse Responses (2009, methodology): “A comparison of the measurement results to an efficient method proposed by Farina [12] showed that the MIRS technique achieves competitive results in anechoic conditions with regard to signal-to-noise ratio and was better suited in public conditions (for details see [10]).”
Transfer-Function Measurement with Sweeps (2001, methodology): “This possibility already anticipated by Griesinger [16] and described by Farina [15] will be further examined in chapter 6.”