Alternative Null Hypothesis Correlation: A New Approach to Automatic Seismic Event Detection

This article describes a new method of seismic signal detection that improves upon the conventional waveform correlation method. Recent studies suggested that a significant limiting factor in the application of waveform correlation to regional and global scale monitoring is the false alarm rate. The false alarms do not originate from detections on noise but rather from seismic arrivals with unrelated source locations. This article presents results from an approach to waveform correlation that exploits techniques from signal processing and machine learning to improve the accuracy of detecting seismic arrivals. We modify the detection model for waveform correlation such that transient signals from noncollocated seismicity are considered when designing the detectors. The new approach uses waveform templates from known catalog events to train a supervised machine learning algorithm that derives a new set of detectors to represent the unique characteristics of the template waveforms; these new detectors maximize the likelihood of detecting only the desired events, thereby minimizing false alarms. We train a waveform correlation template library for a single three‐component seismic monitoring station. We then review results from applying the new detectors, known as alternate null hypothesis correlation (ANCorr) templates, to a test set of seismic waveforms. We compare ANCorr results with those from application of the conventional waveform correlation matched filter technique.

Null Hypotheses
Harmonic Encoding in Cochlear Implants

Today’s standard in cochlear implant (CI) signal processing is based on incoherent tem- poral envelope and temporal fine structure (TFS) extraction. Incoherent envelopes are suffi- cient for the baseline task of speech recognition in quiet; however, current efforts to improve secondary tasks such as speech recognition in noise, lexical tone discrimination and music perception are fundamentally limited by this processing. Harmonic signals are ubiquitous in speech and music. This thesis argues for the ben- efits of coherent extraction of harmonic envelopes and temporal fine structure. By taking harmonic structure into account when designing an envelope and TFS extraction system, processing artifacts can be minimized and signals can be represented more efficiently with the limited data rate of cochlear implants. Furthermore, the proposed method will open up more possibilities for improved cochlear implant encoding. This thesis is a guide to developing a coherent extraction strategy. Incoherent and co- herent extraction systems are evaluated and a generalized method is defined. This method is then applied to harmonic signal encoding. Performance metrics are defined and evaluated and best designs are suggested.

Music in Cochlear Implants
Coherent Envelope Detection