• How to Make a Simple Tensorflow Speech Recognizer

    In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning. The code for this video is here: https://github.com/llSourcell/tensorflow_speech_recognition_demo Mick's winning code: https://github.com/mickvanhulst/tf_chatbot_lotr The weekly challenge can be found at the end of the 'Make a Game Bot' video: https://www.youtube.com/watch?v=mGYU5t8MO7s More learning resources: https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/...

    published: 09 Dec 2016
  • Voice Recognition As Fast As Possible

    Voice recognition, after years of clunky performance, has finally started seeing widespread adoption. How have improvements made it so popular? Squarespace link: Visit http://squarespace.com/linus and use offer code LINUS to save 10% off your first order. Follow: http://twitter.com/linustech Join the community: http://linustechtips.com

    published: 06 Oct 2015
  • Deep Learning for Speech Recognition (Adam Coates, Baidu)

    The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Here are links to the individual talks and the full live streams for the two days: 1. Foundations of Deep Learning (Hugo Larochelle, Twitter) - https://youtu.be/zij_FTbJHsk 2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) - ht...

    published: 27 Sep 2016
  • AnySong Chord Recognition - how to detect chords within any audio track

    With AnySong Chord Recognition you are able to recognize guitar chords in any song stored on your android device. Get it here: https://play.google.com/store/apps/details?id=com.musprojects.chord Imagine that you have a guitar and burn with the desire to play one loved song. But you don't know the chords. One of the first ideas that can come up to your mind is to surf the Internet. If the song is quite popular, it is likely that you will find numerous versions of guitar chords and tabs. But sometimes, the quality of the chords produced by an amateur leaves much to be desired. You can find yourself in a more difficult situation if the song is not among the latest hits from the top charts, or has just been released. There is always an alternative solution -- to ask a professional musician t...

    published: 30 Sep 2012
  • ROAR - ROS OpenSource Audio Recognizer Demonstration Video

    Video submitted to ICRA 2012 along with paper to show the capabilities of our new audio detection toolkit for learning and recognizing environmental sounds in real-time with robots.

    published: 15 Sep 2011
  • how to convert audio to text! 2018 ! Free and easy!

    For accurate transcription services try https://gotranscript.com/r/19405 . They also offer a free trial so you can familiarize yourself with the quality of their services without having to pay for it. IF you transcribe you could earn too! https://gotranscript.com/we-are-hiring-transcribers/r/19405 This is the easiest and free way to convert audio , mp3 or voice to text. I hope this helps you! Stay tuned by subscribing! Lot more videos to come! if you have any questions, suggestions feel free to comment on! Have a great days ahead! check out more videos here in this playlist : http://www.tinyurl.com/howtoplaylist

    published: 04 Apr 2015
  • How To Find Any Songs Name (+ 2M Face Reveal)

    In this life-changing video, I will tell you how to find the name of ANY SONG EVER ...... (that you can accurately make out the lyrics to, and is popular enough to have had its lyrics written up :/ ). And face reveal too. I've had messages in the past asking me "oi mate, is this you?" and they've been right lol, but I thought I'd finally show my face, cus it's annoying and kinda unhealthy how much some people seem to care. So here, have a look and get past the curiosity :p SUBSCRIBE TO MY 2ND CHANNEL: https://www.youtube.com/channel/UCPip... I need one just in case my channel ever fucks off due to some bullshit copyright strike or something. Not got ANYTHING UP there yet, but it's there as a precaution. Support me on Patreon if you want. That would be cool. https://www.patreon.com/Gr...

    published: 15 May 2016
  • Simple Voice Biometric[Speaker Recognition] in Matlab from Basics

    Download Link:http://www.integratedideas.co.in/?download=simple-voice-biometric-speaker-recognition-code-in-matlab {Note: Sorry for distorted audio in some parts of the video due to audio sharing between matlab and the screencast software) The System is extremely simple and based on dominating frequency ( pitch detection). You should use this tutorial to learn designing voice recognition. Must not be used for Production level Biometric.

    published: 29 Sep 2016
  • Recognizing a Million Voices: Low Dimensional Audio Representations for Speaker Identification

    Recent advances in speaker verification technology have resulted in dramatic performance improvements in both speed and accuracy. Over the past few years, error rates have decreased by a factor of 5 or more. At the same time, the new techniques have resulted in massive speed-ups, which have increased the scale of viable speaker-id systems by several orders of magnitude. These improvements stem from a recent shift in the speaker modeling paradigm. Only a few years ago, the model for each individual speaker was trained using data from only that particular speaker. Now, we make use of large speaker-labeled databases to learn distributions describing inter- and intra-speaker variability. This allow us to reveal the speech characteristics that are important for discriminating between speakers....

    published: 17 Aug 2016
  • audio pattern recognition software

    This video illustrates the ability of ORELIA software to 1/ learn an audio pattern with a single example 2/ automatically recognize similar patterns within the rest of the audio file and other files and 3/ generate meta-tag of the searched audio pattern. See orelia.fr or email contact@orelia.fr for more details on existing solutions of sound recognition and real time monitoring

    published: 02 Aug 2013
  • Daft Punk- Recognizer (Astronaut Cult Club Edit)

    Follow me! http://twitter.com/astronaut_cult I thought I'd spice up this song with a few beats 'n stuff. I thought this particular piece would sound awesome faster, so here it is: a "club" remix. DL: http://www.4shared.com/audio/XqWniFFR/Recognizer__Club_Edit_.html

    published: 16 Dec 2010
  • Daft Punk (Tron) - Recognizer (Audio Video)

    Daft Punk (Tron) - Recognizer (Audio Video) lso visit them google + of my channel D.L.G.A. https://plus.google.com/u/0/117459218751697259068 Subscribe to my channel thanks I hope you like it!

    published: 09 Sep 2017
  • Cameron Macleod - Implementing a Sound Identifier in Python

    Cameron Macleod - Implementing a Sound Identifier in Python [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/implementing-a-sound-identifier-in-python) The talk will go over implementing a Shazam-style sound recogniser using DSP techniques and some fantastic libraries. It will cover implementation, challenges and further steps. The project is still a work in progress and the code is [available on GitHub][1]. It was inspired by the [Over-the-Air Audio Identification talk][2] at FOSDEM 2016. [1]: https://github.com/notexactlyawe/abracadabra [2]: https://fosdem.org/2016/schedule/event/audio_identification/ ----- The talk will go over the journey of implementing a Shazam-style sound recogniser using DSP techniques and some fantastic ...

    published: 28 Jul 2016
  • A Guide to Speech Recognition Algorithms (Part 1)

    Feature Extraction Methods: Perceptual Linear Prediction (PLP) Relative spectra filtering of log domain coefficients PLP (RASTA-PLP) Linear predictive coding (LPC) Predictive cepstral coefficients (LPCC) Mel scale cepstral analysis (MEL) Mel-frequency cepstral coefficients (MFCC) Outdated Power spectral analysis (FFT) First order derivative (DELTA) Energy normalization

    published: 08 Dec 2015
  • Sound recognition empowers the intelligent home to react to what is happening around it

    By empowering smart home devices with the ability to respond to the sounds around them we can create a new generation of products that help us to look after the things, people and pets that are important to us. The first generation of the smart home is about connected experiences. We are working with our customers and partners to develop the next generation of products that provide an intelligent experience. Audio Analytic is a sound recognition software company based in Cambridge, UK and Palo Alto, USA. It's flexible, embedded software platform (ai3) enables a wide range of products to respond to the sounds around them. For more information visit audioanalytic.com

    published: 17 May 2017
  • Sound Recognizer App

    Sound Recognizer - app quickly designed for google Android experiments. https://www.androidexperiments.com/challenge Code available at: https://github.com/Kayohi/SoundRecognizer Android 4.4

    published: 12 Apr 2016
  • Pocket Audio Gesture Recognizer (PAGeR) Prototype

    Final project for Mobile HCI, Spring 2012 at Columbia University. This prototype was designed to analyze the audio input from a microphone, and classify them as distinct gestures. By: Abraham Tseng, Ziheng Zhou, Mo Lin

    published: 10 May 2012
  • Speech recognition on the Raspberry Pi 3

    Using an online tutorial and a bit of digging around, I managed to get my Raspberry Pi 3 doing local speech recognition. It's given a shortlist of phrases and it listens continuously, trying to match them. Running on Raspbian Jessie. Instructions are here: https://wolfpaulus.com/journal/embedded/raspberrypi2-sr/ Thanks to Wolf Paulus for the tutorial!

    published: 11 Jun 2016
  • For Akia - Recognizer

    Music video by For Akia performing Recognizer. (C) 2015 Sony Music Entertainment Denmark A/S http://vevo.ly/ikLQHF

    published: 06 Jul 2015
  • X & 0 - with Audio and Video Recognizer

    C# by Jubo Phalelashvili

    published: 04 Jul 2015
  • Music/Sound Recognition -- Matlab-Simulink-Arduino

    Real time spectral pattern recogonition in simulink. http://willforfang.com/

    published: 26 Jul 2013
  • NVIDIA and Intelligent Voice Speech to Text Recognition Using Deep Learning and GPUs

    Intelligent Voice, a global leader in speech-to-text technology, incorporates GPUs to collect, process, review and analyze audio so users can work from a single interface. ECS: https://www.youtube.com/playlist?list=PLZHnYvH1qtOY5XPITwy3w7djkJe0MHMpq Intelligent Voice was a top tech startup in the 2016 Emerging Companies Summit (ECS). See how companies are revolutionizing robotics, AI, big data, VR, and more with the power of GPUs at ECS. Intelligent Voice: www.intelligentvoice.com

    published: 10 Jun 2016
  • Speech recognition in JS | JavaScript Tutorials | Web Development Tutorials

    Learn to code the speech to text converter in JS. Its actually a speech recognition in JS. Website: http://samsolomonprabu.com/ Channel URL: https://www.youtube.com/channel/UCErON4Z0YyiVHKNtx4BvLfg Source code for "Speech recognition in JS": https://verkkonet.com/downloads/index.php?id=j16

    published: 28 Apr 2016
  • Lecture 12: End-to-End Models for Speech Processing

    Lecture 12 looks at traditional speech recognition systems and motivation for end-to-end models. Also covered are Connectionist Temporal Classification (CTC) and Listen Attend and Spell (LAS), a sequence-to-sequence based model for speech recognition. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question a...

    published: 03 Apr 2017
developed with YouTube
How to Make a Simple Tensorflow Speech Recognizer

How to Make a Simple Tensorflow Speech Recognizer

  • Order:
  • Duration: 7:41
  • Updated: 09 Dec 2016
  • views: 96394
videos
In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning. The code for this video is here: https://github.com/llSourcell/tensorflow_speech_recognition_demo Mick's winning code: https://github.com/mickvanhulst/tf_chatbot_lotr The weekly challenge can be found at the end of the 'Make a Game Bot' video: https://www.youtube.com/watch?v=mGYU5t8MO7s More learning resources: https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/ https://www.youtube.com/watch?v=LFDU2GX4AqM https://www.youtube.com/watch?v=g-sndkf7mCs Please subscribe! And like and comment. That's what keeps me going. And please support me on Patreon! I don't work for anyone, although I did make a one-off video for OpenAI because I love them: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajologyyy Instagram: https://www.instagram.com/llsourcell/
https://wn.com/How_To_Make_A_Simple_Tensorflow_Speech_Recognizer
Voice Recognition As Fast As Possible

Voice Recognition As Fast As Possible

  • Order:
  • Duration: 5:25
  • Updated: 06 Oct 2015
  • views: 182841
videos
Voice recognition, after years of clunky performance, has finally started seeing widespread adoption. How have improvements made it so popular? Squarespace link: Visit http://squarespace.com/linus and use offer code LINUS to save 10% off your first order. Follow: http://twitter.com/linustech Join the community: http://linustechtips.com
https://wn.com/Voice_Recognition_As_Fast_As_Possible
Deep Learning for Speech Recognition (Adam Coates, Baidu)

Deep Learning for Speech Recognition (Adam Coates, Baidu)

  • Order:
  • Duration: 1:31:53
  • Updated: 27 Sep 2016
  • views: 24119
videos
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented deep learning material over the past few years, I have to say that this is one of the best collection of introductory deep learning talks I've yet encountered. Here are links to the individual talks and the full live streams for the two days: 1. Foundations of Deep Learning (Hugo Larochelle, Twitter) - https://youtu.be/zij_FTbJHsk 2. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) - https://youtu.be/u6aEYuemt0M 3. Deep Learning for Natural Language Processing (Richard Socher, Salesforce) - https://youtu.be/oGk1v1jQITw 4. TensorFlow Tutorial (Sherry Moore, Google Brain) - https://youtu.be/Ejec3ID_h0w 5. Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU) - https://youtu.be/rK6bchqeaN8 6. Nuts and Bolts of Applying Deep Learning (Andrew Ng) - https://youtu.be/F1ka6a13S9I 7. Deep Reinforcement Learning (John Schulman, OpenAI) - https://youtu.be/PtAIh9KSnjo 8. Theano Tutorial (Pascal Lamblin, MILA) - https://youtu.be/OU8I1oJ9HhI 9. Deep Learning for Speech Recognition (Adam Coates, Baidu) - https://youtu.be/g-sndkf7mCs 10. Torch Tutorial (Alex Wiltschko, Twitter) - https://youtu.be/L1sHcj3qDNc 11. Sequence to Sequence Deep Learning (Quoc Le, Google) - https://youtu.be/G5RY_SUJih4 12. Foundations and Challenges of Deep Learning (Yoshua Bengio) - https://youtu.be/11rsu_WwZTc Full Day Live Streams: Day 1: https://youtu.be/eyovmAtoUx0 Day 2: https://youtu.be/9dXiAecyJrY Go to http://www.bayareadlschool.org for more information on the event, speaker bios, slides, etc. Huge thanks to the organizers (Shubho Sengupta et al) for making this event happen.
https://wn.com/Deep_Learning_For_Speech_Recognition_(Adam_Coates,_Baidu)
AnySong Chord Recognition - how to detect chords within any audio track

AnySong Chord Recognition - how to detect chords within any audio track

  • Order:
  • Duration: 1:41
  • Updated: 30 Sep 2012
  • views: 252792
videos
With AnySong Chord Recognition you are able to recognize guitar chords in any song stored on your android device. Get it here: https://play.google.com/store/apps/details?id=com.musprojects.chord Imagine that you have a guitar and burn with the desire to play one loved song. But you don't know the chords. One of the first ideas that can come up to your mind is to surf the Internet. If the song is quite popular, it is likely that you will find numerous versions of guitar chords and tabs. But sometimes, the quality of the chords produced by an amateur leaves much to be desired. You can find yourself in a more difficult situation if the song is not among the latest hits from the top charts, or has just been released. There is always an alternative solution -- to ask a professional musician to pick up the guitar chords by ear, or to do it yourself. Fortunately, there is a better, faster and more reliable solution now! Recognize guitar chords to your favorite songs on your Android device with a new application - AnySong Chord Recognition. Just choose an audio file from your music collection, wait a little, and get the desired guitar chords! AnySong Chord Recognition automatically analyzes audio, extracts the chords and shows the appropriate guitar tabs on the screen while the track is playing! For sure, you will be amazed with the user friendly interface and appreciate the quality of the recognized chords!
https://wn.com/Anysong_Chord_Recognition_How_To_Detect_Chords_Within_Any_Audio_Track
ROAR - ROS OpenSource Audio Recognizer Demonstration Video

ROAR - ROS OpenSource Audio Recognizer Demonstration Video

  • Order:
  • Duration: 3:03
  • Updated: 15 Sep 2011
  • views: 880
videos
Video submitted to ICRA 2012 along with paper to show the capabilities of our new audio detection toolkit for learning and recognizing environmental sounds in real-time with robots.
https://wn.com/Roar_Ros_Opensource_Audio_Recognizer_Demonstration_Video
how to convert audio to text! 2018 ! Free and easy!

how to convert audio to text! 2018 ! Free and easy!

  • Order:
  • Duration: 4:44
  • Updated: 04 Apr 2015
  • views: 609005
videos
For accurate transcription services try https://gotranscript.com/r/19405 . They also offer a free trial so you can familiarize yourself with the quality of their services without having to pay for it. IF you transcribe you could earn too! https://gotranscript.com/we-are-hiring-transcribers/r/19405 This is the easiest and free way to convert audio , mp3 or voice to text. I hope this helps you! Stay tuned by subscribing! Lot more videos to come! if you have any questions, suggestions feel free to comment on! Have a great days ahead! check out more videos here in this playlist : http://www.tinyurl.com/howtoplaylist
https://wn.com/How_To_Convert_Audio_To_Text_2018_Free_And_Easy
How To Find Any Songs Name (+ 2M Face Reveal)

How To Find Any Songs Name (+ 2M Face Reveal)

  • Order:
  • Duration: 4:47
  • Updated: 15 May 2016
  • views: 5683737
videos
In this life-changing video, I will tell you how to find the name of ANY SONG EVER ...... (that you can accurately make out the lyrics to, and is popular enough to have had its lyrics written up :/ ). And face reveal too. I've had messages in the past asking me "oi mate, is this you?" and they've been right lol, but I thought I'd finally show my face, cus it's annoying and kinda unhealthy how much some people seem to care. So here, have a look and get past the curiosity :p SUBSCRIBE TO MY 2ND CHANNEL: https://www.youtube.com/channel/UCPip... I need one just in case my channel ever fucks off due to some bullshit copyright strike or something. Not got ANYTHING UP there yet, but it's there as a precaution. Support me on Patreon if you want. That would be cool. https://www.patreon.com/GradeAUnderA Also, here are some of my links, follow me on them if you want. Twitch: http://www.twitch.tv/gradeaundera Twitter: http://www.twitter.com/gradeaundera Facebook: http://www.facebook.com/gradeaundera Steam: GradeAUnderA Reddit username: GradeAUnderA Mother's Maiden Name: GradeAUnderA Instagram: OfficialGradeAUnderA Snapchat: officialgaua - My current phone can't handle Snapchat btw Much love to my Patreon supporters, man. The following people need to help me find all the songs in my past songs, cus a few people have asked in the past, and I can't be arsed to man: Sovve, Andy, Simple Basics, Jack-SF, Bill R, Tom L, Kevin R, Prithvi S, Vee T, Corey D, TiraGenocide, Connor A and Bryan B.
https://wn.com/How_To_Find_Any_Songs_Name_(_2M_Face_Reveal)
Simple Voice Biometric[Speaker Recognition] in Matlab from Basics

Simple Voice Biometric[Speaker Recognition] in Matlab from Basics

  • Order:
  • Duration: 46:58
  • Updated: 29 Sep 2016
  • views: 11862
videos
Download Link:http://www.integratedideas.co.in/?download=simple-voice-biometric-speaker-recognition-code-in-matlab {Note: Sorry for distorted audio in some parts of the video due to audio sharing between matlab and the screencast software) The System is extremely simple and based on dominating frequency ( pitch detection). You should use this tutorial to learn designing voice recognition. Must not be used for Production level Biometric.
https://wn.com/Simple_Voice_Biometric_Speaker_Recognition_In_Matlab_From_Basics
Recognizing a Million Voices: Low Dimensional Audio Representations for Speaker Identification

Recognizing a Million Voices: Low Dimensional Audio Representations for Speaker Identification

  • Order:
  • Duration: 1:51:22
  • Updated: 17 Aug 2016
  • views: 1497
videos
Recent advances in speaker verification technology have resulted in dramatic performance improvements in both speed and accuracy. Over the past few years, error rates have decreased by a factor of 5 or more. At the same time, the new techniques have resulted in massive speed-ups, which have increased the scale of viable speaker-id systems by several orders of magnitude. These improvements stem from a recent shift in the speaker modeling paradigm. Only a few years ago, the model for each individual speaker was trained using data from only that particular speaker. Now, we make use of large speaker-labeled databases to learn distributions describing inter- and intra-speaker variability. This allow us to reveal the speech characteristics that are important for discriminating between speakers. During the 2008 JHU summer workshop, our team has found that speech utterances can be encoded into low dimensional fixed-length vectors that preserve information about speaker identity. This concept of so-called 'i-vectors', which now forms the basis of state-of-the-art systems, enabled new machine learning approaches to be applied to the speaker identification problem. Inter- and intra-speaker variability can now be easily modeled using Bayesian approaches, which leads to superior performance. A new training strategies can now benefit form the simpler statistical model form and the inherent speed-up. In our most recent work, we have retrained the hyperparameters of our Bayesian model using a discriminative objective function that directly addresses the task in speaker verification: discrimination between same-speaker and different-speaker trials. This is the first time such discriminative training has been successfully applied to speaker verification task.
https://wn.com/Recognizing_A_Million_Voices_Low_Dimensional_Audio_Representations_For_Speaker_Identification
audio pattern recognition software

audio pattern recognition software

  • Order:
  • Duration: 5:01
  • Updated: 02 Aug 2013
  • views: 3568
videos
This video illustrates the ability of ORELIA software to 1/ learn an audio pattern with a single example 2/ automatically recognize similar patterns within the rest of the audio file and other files and 3/ generate meta-tag of the searched audio pattern. See orelia.fr or email contact@orelia.fr for more details on existing solutions of sound recognition and real time monitoring
https://wn.com/Audio_Pattern_Recognition_Software
Daft Punk- Recognizer (Astronaut Cult Club Edit)

Daft Punk- Recognizer (Astronaut Cult Club Edit)

  • Order:
  • Duration: 4:24
  • Updated: 16 Dec 2010
  • views: 73943
videos
Follow me! http://twitter.com/astronaut_cult I thought I'd spice up this song with a few beats 'n stuff. I thought this particular piece would sound awesome faster, so here it is: a "club" remix. DL: http://www.4shared.com/audio/XqWniFFR/Recognizer__Club_Edit_.html
https://wn.com/Daft_Punk_Recognizer_(Astronaut_Cult_Club_Edit)
Daft Punk (Tron) - Recognizer (Audio Video)

Daft Punk (Tron) - Recognizer (Audio Video)

  • Order:
  • Duration: 2:39
  • Updated: 09 Sep 2017
  • views: 18
videos
Daft Punk (Tron) - Recognizer (Audio Video) lso visit them google + of my channel D.L.G.A. https://plus.google.com/u/0/117459218751697259068 Subscribe to my channel thanks I hope you like it!
https://wn.com/Daft_Punk_(Tron)_Recognizer_(Audio_Video)
Cameron Macleod - Implementing a Sound Identifier in Python

Cameron Macleod - Implementing a Sound Identifier in Python

  • Order:
  • Duration: 21:54
  • Updated: 28 Jul 2016
  • views: 813
videos
Cameron Macleod - Implementing a Sound Identifier in Python [EuroPython 2016] [18 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/implementing-a-sound-identifier-in-python) The talk will go over implementing a Shazam-style sound recogniser using DSP techniques and some fantastic libraries. It will cover implementation, challenges and further steps. The project is still a work in progress and the code is [available on GitHub][1]. It was inspired by the [Over-the-Air Audio Identification talk][2] at FOSDEM 2016. [1]: https://github.com/notexactlyawe/abracadabra [2]: https://fosdem.org/2016/schedule/event/audio_identification/ ----- The talk will go over the journey of implementing a Shazam-style sound recogniser using DSP techniques and some fantastic libraries. It will cover implementation, challenges and further steps. The project is still a work in progress at the time of proposal and the code is [available on GitHub][1]. It was inspired by the [Over-the-Air Audio Identification talk][2] at FOSDEM 2016. The basic structure of the project consists a classifier that fingerprints audio and stores it in a searchable form and a recogniser that fingerprints a smaller chunk of audio and then searches the stored records to find the most suitable fit for it. The recogniser will be exposed as an API to allow for different front-ends. I will aim to introduce both the field of DSP and concepts behind applications like Shazam in a simple easy-to-understand manner. The audience will not need any prior experience in anything except Python. [1]: https://github.com/notexactlyawe/abracadabra [2]: https://fosdem.org/2016/schedule/event/audio_identification/
https://wn.com/Cameron_Macleod_Implementing_A_Sound_Identifier_In_Python
A Guide to Speech Recognition Algorithms (Part 1)

A Guide to Speech Recognition Algorithms (Part 1)

  • Order:
  • Duration: 10:21
  • Updated: 08 Dec 2015
  • views: 22778
videos
Feature Extraction Methods: Perceptual Linear Prediction (PLP) Relative spectra filtering of log domain coefficients PLP (RASTA-PLP) Linear predictive coding (LPC) Predictive cepstral coefficients (LPCC) Mel scale cepstral analysis (MEL) Mel-frequency cepstral coefficients (MFCC) Outdated Power spectral analysis (FFT) First order derivative (DELTA) Energy normalization
https://wn.com/A_Guide_To_Speech_Recognition_Algorithms_(Part_1)
Sound recognition empowers the intelligent home to react to what is happening around it

Sound recognition empowers the intelligent home to react to what is happening around it

  • Order:
  • Duration: 1:42
  • Updated: 17 May 2017
  • views: 413
videos
By empowering smart home devices with the ability to respond to the sounds around them we can create a new generation of products that help us to look after the things, people and pets that are important to us. The first generation of the smart home is about connected experiences. We are working with our customers and partners to develop the next generation of products that provide an intelligent experience. Audio Analytic is a sound recognition software company based in Cambridge, UK and Palo Alto, USA. It's flexible, embedded software platform (ai3) enables a wide range of products to respond to the sounds around them. For more information visit audioanalytic.com
https://wn.com/Sound_Recognition_Empowers_The_Intelligent_Home_To_React_To_What_Is_Happening_Around_It
Sound Recognizer App

Sound Recognizer App

  • Order:
  • Duration: 2:17
  • Updated: 12 Apr 2016
  • views: 473
videos
Sound Recognizer - app quickly designed for google Android experiments. https://www.androidexperiments.com/challenge Code available at: https://github.com/Kayohi/SoundRecognizer Android 4.4
https://wn.com/Sound_Recognizer_App
Pocket Audio Gesture Recognizer (PAGeR) Prototype

Pocket Audio Gesture Recognizer (PAGeR) Prototype

  • Order:
  • Duration: 1:12
  • Updated: 10 May 2012
  • views: 155
videos
Final project for Mobile HCI, Spring 2012 at Columbia University. This prototype was designed to analyze the audio input from a microphone, and classify them as distinct gestures. By: Abraham Tseng, Ziheng Zhou, Mo Lin
https://wn.com/Pocket_Audio_Gesture_Recognizer_(Pager)_Prototype
Speech recognition on the Raspberry Pi 3

Speech recognition on the Raspberry Pi 3

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  • Duration: 3:39
  • Updated: 11 Jun 2016
  • views: 12827
videos
Using an online tutorial and a bit of digging around, I managed to get my Raspberry Pi 3 doing local speech recognition. It's given a shortlist of phrases and it listens continuously, trying to match them. Running on Raspbian Jessie. Instructions are here: https://wolfpaulus.com/journal/embedded/raspberrypi2-sr/ Thanks to Wolf Paulus for the tutorial!
https://wn.com/Speech_Recognition_On_The_Raspberry_Pi_3
For Akia - Recognizer

For Akia - Recognizer

  • Order:
  • Duration: 3:22
  • Updated: 06 Jul 2015
  • views: 15911
videos
Music video by For Akia performing Recognizer. (C) 2015 Sony Music Entertainment Denmark A/S http://vevo.ly/ikLQHF
https://wn.com/For_Akia_Recognizer
X & 0 - with Audio and Video Recognizer

X & 0 - with Audio and Video Recognizer

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  • Duration: 2:21
  • Updated: 04 Jul 2015
  • views: 51
videos
C# by Jubo Phalelashvili
https://wn.com/X_0_With_Audio_And_Video_Recognizer
Music/Sound Recognition -- Matlab-Simulink-Arduino

Music/Sound Recognition -- Matlab-Simulink-Arduino

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  • Duration: 1:38
  • Updated: 26 Jul 2013
  • views: 6241
videos
Real time spectral pattern recogonition in simulink. http://willforfang.com/
https://wn.com/Music_Sound_Recognition_Matlab_Simulink_Arduino
NVIDIA and Intelligent Voice Speech to Text Recognition Using Deep Learning and GPUs

NVIDIA and Intelligent Voice Speech to Text Recognition Using Deep Learning and GPUs

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  • Duration: 2:36
  • Updated: 10 Jun 2016
  • views: 9960
videos
Intelligent Voice, a global leader in speech-to-text technology, incorporates GPUs to collect, process, review and analyze audio so users can work from a single interface. ECS: https://www.youtube.com/playlist?list=PLZHnYvH1qtOY5XPITwy3w7djkJe0MHMpq Intelligent Voice was a top tech startup in the 2016 Emerging Companies Summit (ECS). See how companies are revolutionizing robotics, AI, big data, VR, and more with the power of GPUs at ECS. Intelligent Voice: www.intelligentvoice.com
https://wn.com/Nvidia_And_Intelligent_Voice_Speech_To_Text_Recognition_Using_Deep_Learning_And_Gpus
Speech recognition in JS | JavaScript Tutorials | Web Development Tutorials

Speech recognition in JS | JavaScript Tutorials | Web Development Tutorials

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  • Duration: 17:13
  • Updated: 28 Apr 2016
  • views: 21950
videos
Learn to code the speech to text converter in JS. Its actually a speech recognition in JS. Website: http://samsolomonprabu.com/ Channel URL: https://www.youtube.com/channel/UCErON4Z0YyiVHKNtx4BvLfg Source code for "Speech recognition in JS": https://verkkonet.com/downloads/index.php?id=j16
https://wn.com/Speech_Recognition_In_Js_|_Javascript_Tutorials_|_Web_Development_Tutorials
Lecture 12: End-to-End Models for Speech Processing

Lecture 12: End-to-End Models for Speech Processing

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  • Duration: 1:16:35
  • Updated: 03 Apr 2017
  • views: 12560
videos
Lecture 12 looks at traditional speech recognition systems and motivation for end-to-end models. Also covered are Connectionist Temporal Classification (CTC) and Listen Attend and Spell (LAS), a sequence-to-sequence based model for speech recognition. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
https://wn.com/Lecture_12_End_To_End_Models_For_Speech_Processing
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