Learning and storing the parts of objects: IMF

Fréin, Ruairí de

doi:10.1109/mlsp.2014.6958926

Cited by 8 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This recording may then be used by the target mobile device to obtain a time delay estimate of when the train arrives, or a platform announcement, relative to the mobile phone. Moreover, peer mobile devices which are closer to some source of interference signal may contribute a corrupted single channel recording of the interference signal [16]. In each of these scenarios the ability to be able to estimate time delays is important.…”

Section: Numerical Evaluationmentioning

confidence: 99%

Tiled time delay estimation in mobile cloud computing environments

Fréin¹

2017

2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Self Cite

View full text Add to dashboard Cite

We present a tiled delay estimation technique in the context of Mobile Cloud Computing (MCC) environments. We examine its accuracy in the presence of multiple sources for (1) sub-sample delays and also (2) in the presence of phase-wrap around. Phase wrap-around is prevalent in MCC because the separation of acoustic sources may be large. We show that tiling a histogram of instantaneous phase estimates can improve delay estimates when phase-wrap around is significantly present and also when multiple sources are present. We report that error in the delay estimator is generally less than 5% of a sample, when the true delay is up to 10 samples for three source mixtures.

show abstract

Section: Numerical Evaluationmentioning

confidence: 99%

Tiled time delay estimation in mobile cloud computing environments

Fréin¹

2017

2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The WDO assumption is generally sufficiently true for DUET to de-mix mixtures of up to four to five speech sources. Similarly, Non-negative Matrix Factorization (NMF) [9]- [11] works well when sources do not over-lap in a high proportion of the TF bins. The Adress algorithm relies on a similar property for music mixtures.…”

Section: Introductionmentioning

confidence: 99%

Remedying Sound Source Separation via Azimuth Discrimination and Re-synthesis

Fréin

2020

2020 31st Irish Signals and Systems Conference (ISSC)

Self Cite

View full text Add to dashboard Cite

Commercially recorded music since the 1950s has been mixed down from many input sound sources to a twochannel reproduction of these sources. The effect of this approach is to assign sources to locations in a stereo field using a panposition for each source. The Adress algorithm is a popular way of extracting individual music sound sources from a stereo mixture. A drawback of the Adress algorithm is that when timefrequency components in the stereo mixture are shared between two or more sources, calculating the inter-aural intensity scaling parameter for each source for that time-frequency component is challenging. We show how to obtain a good quality inverse of the pan-mixing process in the time-frequency components which are shared between different sources using a new method called Redress. We demonstrate that we can estimate how much of each source is active in time-frequency components which are shared between sources for two and three-source music mixtures. The consequence of this is that audible artefacts are not as prominent in the source estimates.

show abstract

Selecting Energy Efficient Cluster-Head Trajectories for Collaborative Mobile Sensing

Loomba

Fréin

Jennings

2014

2015 IEEE Global Communications Conference (GLOBECOM)

Self Cite

View full text Add to dashboard Cite

We study the design of energy-efficient stochastic leader-selection algorithms in environments which are sensed by mobile handsets. This work optimizes the energy usage of the interaction between mobile handsets and an application server. The energy-efficiency of mobile handset communications is of increasing importance given the convergence of mobile computing and cloud computing. In some scenarios the lack of WiFi forces mobile devices to expend additional energy transmitting application specific sensed data over cellular networks. This paper focuses on (1) fairly allocating the overhead of transmitting sensed data to a cellular base station amongst the mobile devices by selecting a cluster-head (Fairness is measured in terms of the relative energy expended); (2) successively improving the location of the selected leader such that the efficiency of the entire system is improved; and finally, (3) allowing the mobile devices to gradually forget what they have learned so that the lifetime of the mobile devices is increased (without significantly affecting energy savings). We present two algorithms in this paper and demonstrate that the battery lifetime associated with sensor data transmissions of the mobile handsets may be increased by 20-40% without incurring a sensing accuracy penalty.

show abstract

Learning and storing the parts of objects: IMF

Cited by 8 publications

References 26 publications

Tiled time delay estimation in mobile cloud computing environments

Tiled time delay estimation in mobile cloud computing environments

Remedying Sound Source Separation via Azimuth Discrimination and Re-synthesis

Selecting Energy Efficient Cluster-Head Trajectories for Collaborative Mobile Sensing

Contact Info

Product

Resources

About