KARAOKE FORMATION Pratik Bhanawat (10bec113) Gunjan Gupta (10bec112)
INTRODUCTION   Karaoke is a Japanese abbreviated compound    word, "kara" comes from "karappo" meaning    empty, and "oke...
INTRODUCTION (cont.)   Invented in the late 1970s, the wild    popularity of karaoke over the years has    swept this for...
Brief Of Algorithm Used   Aim - This program attempts to create    karaoke of a song. It attempts to remove    voice and ...
Brief Of Algorithm Used    Method - First one of the channel is high    pass filtered. This is done to protect the    low...
Brief Of Algorithm Used    Select the high pass filter cut off about    50-75 Hz. If there is too much voice in the    ou...
Drawbacks Output is mono and a bit harsh . Does not remove chorus . Also fails if the voice is not identical in  both c...
So, Let’s Begin
Few Other Algorithms   ALGORITHM – 1    It is a two step process comprising of a training    phase, during which a statis...
Few Other Algorithms   ALGORITHM – II    A statistical model to classify segments of    musical audio into vocal or non-v...
Future Advancement   To Transcribe the lyrics - but this is    currently impractical. Transcription of lyrics    using sp...
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Karaoke formation

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Karaoke is the background music of any Song. Extracting background music and removing vocal part from it is called Karaoke foemation.

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Karaoke formation

  1. 1. KARAOKE FORMATION Pratik Bhanawat (10bec113) Gunjan Gupta (10bec112)
  2. 2. INTRODUCTION Karaoke is a Japanese abbreviated compound word, "kara" comes from "karappo" meaning empty, and "oke" is the abbreviation of "okesutura," or orchestra. Usually, a recorded popular song consists of vocals and accompaniment. Musical works in which only the accompaniment is recorded were named "karaoke." Karaoke singing involves singing to such recorded accompaniments of popular songs in front of a live audience. After the singer chooses a song from a catalogue, lyrics are usually displayed on a monitor, recorded music plays, and its Showtime for the novice pop star.
  3. 3. INTRODUCTION (cont.) Invented in the late 1970s, the wild popularity of karaoke over the years has swept this form of singing into the mainstream throughout the world. Karaoke creates its own culture, while reflecting much about the wider culture and the place of popular music as a media form.
  4. 4. Brief Of Algorithm Used Aim - This program attempts to create karaoke of a song. It attempts to remove voice and then writes it to a new wav file. Principle - This program utilizes the fact that voice is recorded equally in both channels without any stereo effect.
  5. 5. Brief Of Algorithm Used Method - First one of the channel is high pass filtered. This is done to protect the low end bass which is also recorded equally on both channels. Then this channel is subtracted from the other. The common part that is voice gets cancelled.
  6. 6. Brief Of Algorithm Used Select the high pass filter cut off about 50-75 Hz. If there is too much voice in the output reduce this value. If it has too little bass increase this value. Note that by doing so voice will also increase.
  7. 7. Drawbacks Output is mono and a bit harsh . Does not remove chorus . Also fails if the voice is not identical in both channels . It takes only .wav as input. You can convert your .mp3 into .wav using any converter.
  8. 8. So, Let’s Begin
  9. 9. Few Other Algorithms ALGORITHM – 1 It is a two step process comprising of a training phase, during which a statistical model is created for a singer’s voice, and a working phase, during which the starting point of the singing voice is detected and a fixed length of testing data is taken from that point. Audio features extracted from this data are then compared against the existing singers’ models to perform singer identification. Singing voice detection is achieved by extracting features of energy, average zero-crossing rate (ZCR), harmonic coefficients and spectral flux computed at regular intervals which are then compared against a set of predetermined thresholds.
  10. 10. Few Other Algorithms ALGORITHM – II A statistical model to classify segments of musical audio into vocal or non-vocal using a Hidden Markov Model (HMM) classifier. The feature extraction is based on sub-band processing that uses the log frequency power coefficients (LFPC) to provide an indication of the energy distribution among sub-bands. The training model also takes into account tempo and song structure information in song modeling based on the observed variations in intra-song signal characteristics.
  11. 11. Future Advancement To Transcribe the lyrics - but this is currently impractical. Transcription of lyrics using speech recognition is an extremely challenging task as singing differs from speech in many ways. The phonetic and timing modification, presence of meaningless syllables often employed by singers and interference of the instrumental background would make an acoustic classifier trained on normal speech a poor match to the acoustics of the sung vocal line.

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