Low Frequency Tilt Seismology with a Precision Ground Rotation Sensor

We describe measurements of the rotational component of teleseismic surface waves using an inertial high-precision ground-rotation-sensor installed at the LIGO Hanford Observatory (LHO). The sensor has a noise floor of 0.4 nrad$/ \sqrt{\rm Hz}$ at 50 mHz and a translational coupling of less than 1 $\mu$rad/m enabling translation-free measurement of small rotations. We present observations of the rotational motion from Rayleigh waves of six teleseismic events from varied locations and with magnitudes ranging from M6.7 to M7.9. These events were used to estimate phase dispersion curves which shows agreement with a similar analysis done with an array of three STS-2 seismometers also located at LHO

Wireless recording of the calls of Rousettus aegyptiacus and their reproduction using electrostatic transducers

Bats are capable of imaging their surroundings in great detail using echolocation. To apply similar methods to human engineering systems requires the capability to measure and recreate the signals used, and to understand the processing applied to returning echoes. In this work, the emitted and reflected echolocation signals of Rousettus aegyptiacus are recorded while the bat is in flight, using a wireless sensor mounted on the bat. The sensor is designed to replicate the acoustic gain control which bats are known to use, applying a gain to returning echoes that is dependent on the incurred time delay. Employing this technique allows emitted and reflected echolocation calls, which have a wide dynamic range, to be recorded. The recorded echoes demonstrate the complexity of environment reconstruction using echolocation. The sensor is also used to make accurate recordings of the emitted calls, and these calls are recreated in the laboratory using custom-built wideband electrostatic transducers, allied with a spectral equalization technique. This technique is further demonstrated by recreating multi-harmonic bioinspired FM chirps. The ability to record and accurately synthesize echolocation calls enables the exploitation of biological signals in human engineering systems for sonar, materials characterization and imaging

Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be consistent with our conference submission (no content change other than typo fixes