Head Related Transfer Functions (HRTFs) are essential to research in the field of binaural sound reproduction. HRTFs can be obtained from existing databases, but there are also a few rare facilities around the world where you can get your very own HRTFs measured. This is what a group of scientists from AudioLabs and Fraunhofer IIS ventured out to do last week.
This research investigates the suitability of Non-Negative Matrix Factor Deconvolution (NMFD) for source separation of drum solo recordings (especially breakbeats) into their constituent drum sound events. Intuitively speaking, NMFD models the drum recording with a small number of template sound events, ideally corresponding to the sound of individual drum instruments. The accompanying website lists several audio examples of decompositions of iconic breakbeats as well as selected items from the "IDMT-SMT-Drums" test set. This research has also been presented in a talk on "breakbeat science" given by Jason Hockman at the Ableton Loop conference (see the video). Visit the Website for more information and audio examples!
Speech intelligibility is an important aspect of speech transmission but often when speech coding standards are compared only the quality is evaluated using perceptual tests. In this study, the performance of three wideband speech coding standards, adaptive multi-rate wideband (AMR-WB), G.718, and enhanced voice services (EVS), is evaluated in a subjective intelligibility test. The test covers different packet loss conditions as well as a near-end background noise condition. Additionally, an objective quality evaluation in different packet loss conditions is conducted. All of the test conditions extend beyond the specification range to evaluate the attainable performance of the codecs in extreme conditions. The results of the subjective tests show that both EVS and G.718 are better in terms of intelligibility than AMR-WB. EVS attains the same performance as G.718 with lower algorithmic delay.
One central problem in music signal processing is the decomposition of a given audio recording of polyphonic music into sound components that correspond to musical voices, instrument tracks, or individual note events. One main challenge arises from the fact that musical sources are highly correlated, share the same harmonies and follow the same rhythmic patterns. In current research, we use additional cues such as the musical score to support the separation process. The score also provides a natural way for a user to interact with the separated sound events.