Douglas B. Holmes

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Douglas Holmes (Doctorate of Music in composition with a specialty computer music and minor in multimedia)

Diversity, Equity, and Inclusion: Live Music and community.



Statement of objective


Diversity, Equity, and Inclusion

  • Implicit Bias
    • Racial Bias
    • Gender Bias
    • Age Bias
    • Video
  • Diversity
    • Internal diversity
    • External diversity
    • Organizational diversity
    • Model
  • Equity
  • Inclusion
  • Accessibility
    • information
    • activities
    • environments
    • Video
  • NASA: explore from space to sound

    United Nations: Universal Declaratuion of Human Rights


    “Music is the universal language of mankind”

    -Henry Wadsworth Longfellow (1807-1882)

  • 5-year study
    • Scientists from around the globe
    • Discography (archival recordings)
    • Ethnography (customs and cultures)
  • Music is associated with universal behaviors
    • Infant care, and healing
    • Dance, procession, and ritual
    • Love and mourning
  • An article in The Harvard Gazette by Jed Gottlieb
  • Mozart’s Twelve Variations on "Ah vous dirai-je, Maman". Based on the French melody with the same name (fr.1761)


    Music has a way of crossing cultural divisions and influences one another

  • From Chapter 6
    • Music
    • America culture
  • Cultural influences on music
    • Migration
    • Youth culture
    • Racial Integration
  • Musical influences on Culture
    • Race
    • Morality
    • Gender

    Live music and the communal experience

  • connections between live music and urban planning
    • Social capital: bonding and bridging
    • Public engagement (Inclusion)
    • Identity
    • Cultural values, and Talent development
  • the magic of live music
    • Mutual turning-in
    • Greater sense of belonging
    • Synchronization of movement
    • Emotional satisfaction
  • Live has energy and immediacy that recorded formats cannot reproduce
    • intensity and magnification of energy
    • power of dynamic and timbreal contrast
    • Exchange between performers/audience


    What kinds of music do you like?



    What Concerts do you attended?



    Who do you share musical experiences with?



    Reggae rise up Florida (1st festival after lock down)

  • Covid shuts down live events
    • Sports: Covid bubbles, cardboard fans, piped in crowd noises.
    • Concerts: Internet broadcast, zoom parties and gatherings
    • Broadcasted funerals
    • Isolation
  • Reggae is a music genre
    • originated in Jamaica
    • Influenced by Ska, Calypso, rhythm and blues, and other Afro-Caribbean
    • perspectives tied to the Rastafari religion
    • political and calls out for social justice, peace, and harmony
  • Damien Marley (1978) 4 Grammy awards
  • The hiring of visual artists who painted and built grand moving objects/murals throughout the festival expanded the concert. Painters were positioned on the main stage and worked during band performances, art form influencing one another (the work was auctioned for charities).

    The festival was Accessible and inclusive, handicap section with clear paths around the festival, sign interpreters at every stage (part of the music), and quiet areas.


    How big is the music industry in our economy? And how about diversity within the industry?

  • Job and Benefits 2021
    • $170B Total economic contribution to GDP
    • 2,466,026 total jobs supported
    • 236,269 total music related establishments
  • USC Annenberg report - Music Industry
    • Leadership positions lean toward white men
    • Senior management of music groups show the highest diversity
    • From 101 music companies over all 19.8% UR, 7.5% Black, and 35.3% Woman
  • Information is Beautiful
    • Graph on gender and diversity in companies
    • Diversity in Tech employment
    • 2014 - 2017
    • link

    How can employment sectors become more diversified and inclusive?

  • Governmental support
    • high-speed Internet access
    • Equity funding for education
    • Industry incentives
  • Corporate support
    • Robust mentorship/internship programs
    • Provide flexible pathways to leadership roles
    • Enlist diverse vendors
  • Community support
    • Internships/apprenticeships
    • Specialized Training
    • Community media studios

    Analytic Tools

  • Video analytics and descriptors
    • Global video data
    • Advanced discovery
    • Streaming video OTT
    • Studio and content data
  • Music analytics
    • Global content data
    • Audio on demand
    • Music discovery
  • Other entertainment sectors
    • Auto solutions
    • Sports solutions
  • Statistics from your video
    • Multiple timelines assigned to quick keys (eg. identify when characters speak)
    • Export labeled and identified segments
    • Synchronize audio and text
    • Draw on video
  • Analytics
    • Spotify Analytics
    • YouTube Analytics
    • Social Media Analytics
    • Radio Airplay Analytics
    • Viberate Chart (cross-channel)
    • Side-by-side Label comparison (2022)
    • Beatport Chart (artists, labels and tracks)
    • Radio Airplay Chart
  • FESTIVALS (6K+ pages)
    • Festival pages with basic info
    • Festival Analytics
    • Side-by-side festival comparison (2022)

    Deep learning and big data programming

  • Necessary python libraries
    • Numpy
    • Pandas(data analysis)
    • Skimage
    • OpenCV
  • Summarization of steps
    • Import the video, extract frames Label a few images (training the model)
    • Build our model on training data
    • Make predictions for the remaining images
    • Calculate the screen time
  • Results (2 character cartoon)
    • About 88% on the validation data
    • 64% on the test data using this model
    • Extract more frames
    • label them accordingly, and use them for training the model
  • Necessary python libraries
    • Numpy
    • Librosa(audio analysis)
    • IPython.display.Audio
    • matplotlib
    • sklearn (mach learning)
    • keras(deep learning API)
  • Summarization part 1 (introduction to DSP)
    • Import audio files
    • Convert time domain to frequency domain
    • Spectral feature extraction: centroid, Rolloff, Bandwidth
    • Build model from marsyas dataset(1000 tracks, 10 genres)
    • Create database
  • Summarization part 2 (genre identification)
    • Artificial Neural Network(ANN)
    • Convolutional Neural Networks (CNN)
    • Automation of spectral processing
    • File and directory management
    • Image Augmentation
  • Convolution Neural Networks are made of neurons that have learnable weights and biases. Each neuron input vector magnitude can be multiplied following a non-linear path. The network is expressed as single differentiable score function. Convolution Neural Networks (animated gif)