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

Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription

In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated

The Life and Times of Peter Briggins

This article draws attention to a little known and rarely used historical source, the diary of the early eighteenth-century London Friend Peter Briggins. Four areas of Briggins\u27 life are examined: his business, religious, family and leisure activities. It is suggested that the examination of sources such as diaries and personal correspondence can shed new light on the nature of seventeenth and early eighteenth-century Quakerism. In particular, such material can enable the development of a more subtle picture of the relationships that existed between Friends and the communities in which they lived

Controls on Carbon Cycling in Upland Blanket Peat Soils

Peatlands are a globally important, terrestrial store of carbon and the UK is recognised as an internationally significant holder of peatlands. Of all the kinds of peatland found in the UK, blanket bogs are dominant, representing 87% of the UK’s peatland area. The UK’s peatlands, in contrast to many other areas of boreal/temperate peat, are relatively accessible and as such have been subject to land-management pressures for many thousands of years. These management pressures have led to the deterioration of many peatlands in the UK, with only 1% of England’s peatlands being considered ‘pristine’ in a Natural England report (Natural England, 2010). Climate change and increasing land-use pressures are predicted to affect all UK peatlands in coming years. As such, studies of the drivers of carbon cycling on UK peatlands are being undertaken in order to help in the construction of models to predict the dynamics of peatland carbon balance. These models will subsequently enable land-managers and policy makers to take informed decisions regarding peatland management and carbon storage. One such model of peatland carbon balance is the Durham Carbon Model, which uses a mass balance between fluxes of carbon in and out of a peatland in order to estimate its net carbon budget. While the Durham Carbon Model is able to deal with the effects of some aspects of land-management on peatland carbon balance, there remain a number of important drivers as yet unaccounted for in the model. As such, the remit of this thesis was to conduct in-situ, experiments in order to provide additional data on peatland carbon cycling with a view to incorporating these drivers into the model. Specifically, this research examines three areas as yet unaccounted for in the Durham Carbon Model: altitude, vegetation and diurnal processes. These factors are considered relative to CO2 flux and, in some cases, soil pore water dissolved organic carbon concentration. Additional experiments were also performed to determine whether empirical models of CO2 flux can be physically interpretable. Results obtained for this thesis suggest that the most important factor in predicting CO2 flux on blanket peat soils is vegetation type and vegetation mediated processes, i.e. photosynthetic controls on respiration. Moreover, the relationship between respiration and photosynthesis was found across a range of other factors and temporal scales. In addition to vegetation, altitude was found to significantly affect CO2 for some vegetation types. Therefore, both of these factors are to be incorporated into the Durham Carbon Model. Experiments suggested that empirical models of CO2 flux can be physically interpretable. The results of the diurnal experiment gave evidence to support the hypothesis that some component of the relationship between photosynthesis and respiration is temporally lagged, perhaps by 3 hours. However, the results were not unequivocal and thus further work is needed to fully examine some of the results presented herein