1. Loris for Your Cough Roshan Mansinghani, Esmeralda Martinez, James McDougall, Travis McPhail Results: The noise frequencies were completely removed including the low frequency components. The low frequency, long lived tracks were preserved. Most of the upper harmonic information was also lost. Acknowledgements: Kelly Fitz, Lippold Haken, and other Cerl Sound Group Members. Susanne Lefver, developer of Fossa. Dr.Baraniuk. Approach: Record a simple audio file, such as a clarinet playing a single note with a cough in the middle. Break the file up into short-time windows. Analyze the frequency content of each window separately and remove unwanted noise. Reassemble the file with as little distortion to the music as possible. Goal: Analyze the possibility of removing short-time noise, such as coughs or sneezes, from live recorded audio files. Future Research: Improved algorithm to not remove upper harmonics. Automated removal of noise. Use Loris sound morphing capabilities to morph two or more sound files. Possible removal of other types of extraneous noise (cell phone, keys, clapping, etc.) Motivation: Often during live recorded concerts people cough or sneeze. This noise appears in the recording and stands out from the surrounding music. Background: 1.) The McAulay and Quatieri (MQ) Method Window off overlapping sections of the signal. Compute Fourier Transform of each window and find dominant frequencies (partials). Connect partials from each window to track their progression through time. Figure 2 [Fitz] Interpolate between connected points to generate a smooth track. Use the tracks to develop cosine terms with time-varying amplitude, phase, and frequency. Re-assemble sound by summing cosine terms. 2.) Reassigned Bandwidth-Enhanced Method of Additive Synthesis [Fitz] Implements MQ method. Differs in handling noise as to eliminate introduced errors: Short, “jittery” tracks can be considered noise. Removes short tracks while still conserving signal energy and frequency centers. When re-assembling noisy signals articles are introduced into the reconstructed signal. Implementation: Loris Sound Software A C++ library implementing the Bandwidth-Enhanced Model. Handles windowing of signal using a Kaiser window. Increases bandwidth of tracks in the vicinity of the rejected track. Increases bandwidth using Bandwidth-Enhanced Oscillators. Before and after removal of noise tracks [Fitz] Effect of Bandwidth-Enhanced Oscillator on a single frequency[Fitz] Computes Short-time Fourier Transforms. Tracks the progression of Partials through time. Uses the reconstruction process defined in the MQ model. Graphical User Interface (Fossa) for viewing amplitude and frequency tracks. Magnitude of Kaiser WindowFrequency Response of Kaiser Window Frequency Track for a clarinet playing a single note. Spectrogram of clarinet and cough Our Algorithm: Noise is represented by short duration tracks. If a track had large gaps (multiple windows) between partials it was broken into smaller pieces. Each track was analyzed for duration. If a track’s duration was less than a threshold it was removed. The signal was reconstructed using standard MQ methods. Partial tracks before filtering Partial tracks after filtering Spectrogram of clarinet with cough removed Contact Information: Roshan Mansinghani: rosh@rice.edurosh@rice.edu Esmerelda Martinez: esme@rice.eduesme@rice.edu James McDougall: jamesmcd@rice.edujamesmcd@rice.edu Travis McPhail: tjice@rice.edu