Justin Salamon
  • Home
  • News
  • Research
  • Publications
  • Code/Data
  • Melody Extraction
  • PhD Thesis
  • Contact
    • Music
    • Music Technology

Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series

19/7/2018

0 Comments

 
Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.

For example, we used the Noise Profiler to rapidly explore and visualize noise patterns in NYC during weekdays versus weekends across multiple locations, using time series data from SONYC noise sensors:
Picture
Noise patterns on weekdays vs. weekends from a variety of locations in NYC. Time series data from SONYC noise sensors explored and visualized using the Noise Profiler tool built with Time Lattice.

For further details see our paper:

Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
F. Miranda, M. Lage, H. Doraiswamy, C. Mydlarz, J. Salamon, Y. Lockerman, J. Freire, C. Silva
Computer Graphics Forum (EuroVis '18), 37(3), 2018, 13-22
[Wiley][PDF][BibTeX]
0 Comments

SONYC featured in New York Times, NPR, Wired and more

7/11/2016

0 Comments

 
Picture
Today SONYC was featured on several major news outlets including the New York Times, NPR and Wired! This follows NYU's press release about the official launch of the SONYC project.

Needless to say I'm thrilled about the coverage the project's launch is receiving. Hopefully it is a sign of the great things yet to come from this project, though, I should note, it has already resulted in several scientific publications.

Here's the complete list of media articles (that I could find) covering SONYC. The WNYC radio segment includes a few words from yours truly :)

Picture
To Create a Quieter City, They’re Recording the Sounds of New York
Picture
BBC World Service - World Update (first minute, then from 36:21)
Picture
Mapping New York City's Excessively Loud Sounds​
Picture
New York, come usare i microfoni per una città più silenziosa​
Picture
Scientists Are Tracking New York Noisiness in Order to Quiet It Down
Picture
NYU Scientists are Trying to Reduce Noise Pollution in New York City
Picture
Researchers Are Recording New York to Make it Quieter
Picture
Sounds of New York City (German Public Radio)
Picture
NYC’s $5 Million Noise Pollution Project
Picture
Mapping the Sounds of New York City Streets
Picture
New UrbanEars project has NYU teaming up with Ohio State to battle noise pollution
Picture
NYU Launches Research Initiative to Combat NYC Noise Pollution
Picture
Smart microphones are recording city sounds to help create a quieter New York
Picture
NYU Moves Forward with Study of City Noise
Picture
How to Take on NYC’s Scary Noise Problem
Picture
Research Initiative Looks to Tame Urban Noise Pollution
If you're interested to learn more about the SONYC project have a look at the SONYC website. You can also check out the SONYC intro video:
0 Comments

Meet SONYC: Sounds of New York City

15/10/2015

0 Comments

 
Over the past two years I've been working together with a fantastic team of researchers on the SONYC: Sounds of New York City project. Check out our new video!

The objectives of SONYC are to create technological solutions for: (1) the systematic, constant monitoring of noise pollution at city scale; (2) the accurate description of acoustic environments in terms of its composing sources; (3) broadening citizen participation in noise reporting and mitigation; and (4) enabling city agencies to take effective, information-driven action for noise mitigation.

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. It has been estimated that 9 out of 10 adults in New York City (NYC) are exposed to excessive noise levels, i.e. beyond the limit of what the EPA considers to be harmful. When applied to U.S. cities of more than 4 million inhabitants, such estimates extend to over 72 million urban residents.

To learn more about the SONYC project please check out the project website: wp.nyu.edu/sonyc

To read our publications on automatic urban sound classification as well as the development of low-cost, high-quality acoustic sensors, check out the project's publication page: wp.nyu.edu/sonyc/publications
0 Comments

    NEWS

    Machine listening research, code, data & hacks!

    Archives

    April 2022
    November 2021
    October 2021
    June 2021
    January 2021
    October 2020
    June 2020
    May 2020
    April 2020
    January 2020
    November 2019
    October 2019
    June 2019
    May 2019
    March 2019
    February 2019
    January 2019
    November 2018
    October 2018
    August 2018
    July 2018
    May 2018
    April 2018
    February 2018
    October 2017
    August 2017
    July 2017
    June 2017
    April 2017
    March 2017
    January 2017
    December 2016
    November 2016
    October 2016
    August 2016
    June 2016
    May 2016
    April 2016
    February 2016
    January 2016
    November 2015
    October 2015
    July 2015
    June 2015
    April 2015
    February 2015
    November 2014
    October 2014
    September 2014
    June 2014
    April 2014
    March 2014
    February 2014
    December 2013
    September 2013
    July 2013
    May 2013
    February 2013
    January 2013
    December 2012
    November 2012
    October 2012
    August 2012
    July 2012
    June 2012

    Categories

    All
    ACM MM'13
    ACM MM'14
    Acoustic Ecology
    Acoustic Event Detection
    Acoustic Sensing
    AES
    Applied Acoustics
    Article
    Audio-annotator
    Audio To Midi
    Auditory Scene Analysis
    Avian
    Award
    Baseball
    Beer
    Best Oral Presentation
    Best Paper Award
    Best Student Paper Award
    BigApps
    Bioacoustics
    BirdVox
    Book
    Chapter
    CHI
    Citizen Science
    Classification
    Computer Vision
    Conference
    Connected Cities
    Convolutional Neural Networks
    Cornell Lab Of Ornithology
    Coursera
    Cover Detection
    CREPE
    Crowdcrafting
    Crowdsourcing
    CUSP
    CVPR
    Data Augmentation
    Data Science
    Dataset
    Data Structures
    Dcase
    Deep Learning
    Domain
    Education
    Entrepreneurship
    Environmental Sound
    Essentia
    Eusipco
    Eusipco2015
    Evaluation
    Few-shot Learning
    Flight Calls
    Girl Scouts
    Grant
    Hackathon
    Hackday
    Hackfest
    HCI
    Hildegard Von Bingen
    ICASSP
    ICASSP 2020
    IEEE Signal Processing Letters
    Ieee Spm
    Indian Classical Music
    Interface
    Interspeech
    Interview
    Ismir 2012
    Ismir2014
    Ismir2015
    Ismir2016
    Ismir2017
    Ismir2020
    ITP
    Jams
    Javascript
    JNMR
    Journal
    Machine Learning
    Machine Listening
    Map
    Media
    Melodia
    Melody Extraction
    Metric Learning
    Midi
    Migration Monitoring
    MIR
    Mir_eval
    MOOC
    MTG-QBH
    Music Informatics
    Music Information Retrieval
    Music Similarity
    National Science Foundation
    Neumerator
    New York Times
    Noise Pollution
    Notebook
    NPR
    NSF
    NYC
    NYU
    Open Source
    Pitch
    Pitch Contours
    Pitch Tracking
    Plos One
    Plug In
    Plug-in
    Presentation
    Press
    PRI
    Prosody
    Publication
    Python
    Query By Humming
    Query-by-humming
    Radio
    Representation Learning
    Research
    Robots
    Scaper
    Science And The City
    Science Friday
    Self-supervision
    Sensor Network
    Sensors
    Sight And Sound Workshop
    Smart Cities
    Software
    SONYC
    Sound Classification
    Sound Education
    Sound Event Detection
    Soundscape
    Sounds Of New York City
    Sound Workshop
    Speech
    STEM
    Synthesis
    Taste Of Science
    Taxonomy
    Technical Report
    Time Series
    Tonic ID
    Tony
    Tutorial
    Unsupervised Feature Learning
    Urban
    Urban Sound Analysis
    Urban Sound Tagging
    Vamp
    Version Identification
    Visualization
    Vocaloid
    Vocoder
    Warblers
    Wav To Midi
    Welcome
    Wired
    WNYC
    Women In Science
    Workshop
    World Domination
    Wsf14
    Youtube

    RSS Feed

Powered by Create your own unique website with customizable templates.
  • Home
  • News
  • Research
  • Publications
  • Code/Data
  • Melody Extraction
  • PhD Thesis
  • Contact
    • Music
    • Music Technology