Loris for Your Cough Roshan Mansinghani, Esmeralda Martinez, James McDougall, Travis McPhail

1. Spectrogram of clarinet and cough Spectrogram of clarinet with cough removed Figure 2 [Fitz] Loris for Your Cough Roshan Mansinghani, Esmeralda Martinez, James McDougall, Travis McPhail Goal: Analyze the possibility of removing short-time noise, such as coughs or sneezes, from live recorded audio files. • 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. • 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. Motivation: Often during live recorded concerts people cough or sneeze. This noise appears in the recording and stands out from the surrounding music. Partial tracks before filtering Before and after removal of noise tracks [Fitz] • Increases bandwidth of tracks in the vicinity of the rejected track. • Increases bandwidth using Bandwidth-Enhanced Oscillators. • 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. Partial tracks after filtering • 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. Effect of Bandwidth-Enhanced Oscillator on a single frequency[Fitz] • Background: • 1.) TheMcAulay 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. • Implementation: Loris Sound Software • A C++ library implementing the Bandwidth-Enhanced Model. • Handles windowing of signal using a Kaiser window. • 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.) Magnitude of Kaiser Window Frequency Response of Kaiser Window • 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. • 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. • Contact Information: • Roshan Mansinghani: rosh@rice.edu • Esmerelda Martinez: esme@rice.edu • James McDougall: jamesmcd@rice.edu • Travis McPhail: tjice@rice.edu When re-assembling noisy signals articles are introduced into the reconstructed signal. Frequency Track for a clarinet playing a single note. Acknowledgements:Kelly Fitz, Lippold Haken, and other Cerl Sound Group Members. Susanne Lefver, developer of Fossa. Dr.Baraniuk.