Example-based audio editing

Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques

An iterative least-squares technique for dereverberation

Some recent dereverberation approaches that have been effective for ASR applications, model reverberation as a linear convolution operation in the spectral domain, and derive a factorization to decompose spectra of reverberated speech in to those of clean speech and of the room-response filter. Typically, a general NMF framework is employed for this. In this work 1 we present an alternative to NMF and propose an iterative least-squares deconvolution technique for spectral factorization. We propose an efficient algorithm for this and experimentally demonstrate it’s effectiveness in improving ASR performance. The new method results in 40-50 % relative reduction in word error rates over standard baselines on artificially reverberated speech