Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction

The state of the art in music source separation employs neural networks trained in a supervised fashion on multi-track databases to estimate the sources from a given mixture. With only few datasets available, often extensive data augmentation is used to combat overfitting. Mixing random tracks, however, can even reduce separation performance as instruments in real music are strongly correlated. The key concept in our approach is that source estimates of an optimal separator should be indistinguishable from real source signals. Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples. This way, we can also use unpaired source and mixture recordings without the drawbacks of creating unrealistic music mixtures. Our framework is widely applicable as it does not assume a specific network architecture or number of sources. To our knowledge, this is the first adoption of adversarial training for music source separation. In a prototype experiment for singing voice separation, separation performance increases with our approach compared to purely supervised training.Comment: 5 pages, 2 figures, 1 table. Final version of manuscript accepted for 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Implementation available at https://github.com/f90/AdversarialAudioSeparatio

Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages

Lyrics alignment gained considerable attention in recent years. State-of-the-art systems either re-use established speech recognition toolkits, or design end-to-end solutions involving a Connectionist Temporal Classification (CTC) loss. However, both approaches suffer from specific weaknesses: toolkits are known for their complexity, and CTC systems use a loss designed for transcription which can limit alignment accuracy. In this paper, we use instead a contrastive learning procedure that derives cross-modal embeddings linking the audio and text domains. This way, we obtain a novel system that is simple to train end-to-end, can make use of weakly annotated training data, jointly learns a powerful text model, and is tailored to alignment. The system is not only the first to yield an average absolute error below 0.2 seconds on the standard Jamendo dataset but it is also robust to other languages, even when trained on English data only. Finally, we release word-level alignments for the JamendoLyrics Multi-Lang dataset.Comment: 5 pages, accepted at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 202

Development of Life Support System Technologies for Human Lunar Missions

With the Preliminary Design Review (PDR) for the Orion Crew Exploration Vehicle planned to be completed in 2009, Exploration Life Support (ELS), a technology development project under the National Aeronautics and Space Administration s (NASA) Exploration Technology Development Program, is focusing its efforts on needs for human lunar missions. The ELS Project s goal is to develop and mature a suite of Environmental Control and Life Support System (ECLSS) technologies for potential use on human spacecraft under development in support of U.S. Space Exploration Policy. ELS technology development is directed at three major vehicle projects within NASA s Constellation Program (CxP): the Orion Crew Exploration Vehicle (CEV), the Altair Lunar Lander and Lunar Surface Systems, including habitats and pressurized rovers. The ELS Project includes four technical elements: Atmosphere Revitalization Systems, Water Recovery Systems, Waste Management Systems and Habitation Engineering, and two cross cutting elements, Systems Integration, Modeling and Analysis, and Validation and Testing. This paper will provide an overview of the ELS Project, connectivity with its customers and an update to content within its technology development portfolio with focus on human lunar missions

Exploration Life Support Technology Development for Lunar Missions

Exploration Life Support (ELS) is one of NASA's Exploration Technology Development Projects. ELS plans, coordinates and implements the development of new life support technologies for human exploration missions as outlined in NASA's Vision for Space Exploration. ELS technology development currently supports three major projects of the Constellation Program - the Orion Crew Exploration Vehicle (CEV), the Altair Lunar Lander and Lunar Surface Systems. ELS content includes Air Revitalization Systems (ARS), Water Recovery Systems (WRS), Waste Management Systems (WMS), Habitation Engineering, Systems Integration, Modeling and Analysis (SIMA), and Validation and Testing. The primary goal of the ELS project is to provide different technology options to Constellation which fill gaps or provide substantial improvements over the state-of-the-art in life support systems. Since the Constellation missions are so challenging, mass, power, and volume must be reduced from Space Shuttle and Space Station technologies. Systems engineering analysis also optimizes the overall architecture by considering all interfaces with the life support system and potential for reduction or reuse of resources. For long duration missions, technologies which aid in closure of air and water loops with increased reliability are essential as well as techniques to minimize or deal with waste. The ELS project utilizes in-house efforts at five NASA centers, aerospace industry contracts, Small Business Innovative Research contracts and other means to develop advanced life support technologies. Testing, analysis and reduced gravity flight experiments are also conducted at the NASA field centers. This paper gives a current status of technologies under development by ELS and relates them to the Constellation customers who will eventually use them

Assessment of the Impacts of ACLS on the ISS Life Support System using Dynamic Simulations in V-HAB

The Advanced Closed Loop System (ACLS) is currently under development by Airbus Defense and Space and is slated for launch to the International Space Station (ISS) in 2017. The addition of new hardware into an already complex system such as the ISS life support system (LSS) always poses operational risks. It is therefore important to understand the impacts ACLS will have on the existing systems to ensure smooth operations for the ISS. This analysis can be done by using dynamic computer simulations and one possible tool for such a simulation is Virtual Habitat (V-HAB). Based on Matlab (Registered Trademark) V-HAB has been under development at the Institute of Astronautics of the Technical University Munich (TUM) since 2006 and in the past has been successfully used to simulate the ISS life support systems. The existing V-HAB ISS simulation model treated the interior volume of the space station as one large ideally-stirred container. This model was improved to allow the calculation of the atmospheric composition inside the individual modules of the ISS by splitting it into ten distinct volumes. The virtual volumes are connected by a simulation of the inter-module ventilation flows. This allows for a combined simulation of the LSS hardware and the atmospheric composition aboard the ISS. A dynamic model of ACLS is added to the ISS simulation and different operating modes for both ACLS and the existing ISS life support systems are studied to determine the impacts of ACLS on the rest of the system. The results suggest that the US, Russian and ACLS CO2 systems can operate at the same time without impeding each other. Furthermore, based on the results of this analysis, the US and ACLS Sabatier systems can be operated in parallel as well to achieve the highest possible CO2 recycling together with a low CO2 concentration