1. ANIL ALEXANDER1, OSCAR FORTH1 AND DONALD TUNSTALL2 1 Oxford Wave Research Ltd, United Kingdom {anil|oscar}@oxfordwaveresearch.com 2 Digital Audio Corporation, USA dtunstall@dacaudio.com Audio Engineering Society 46th Conference on Audio Forensics Denver, Colorado June 14-16, 2012 MUSIC AND NOISE FINGERPRINTING AND REFERENCE CANCELLATION APPLIED TO FORENSIC AUDIO ENHANCEMENT

2. Introduction In surveillance audio recordings, it is common to come across: Interfering music or a television playing in the background in locations like pubs, cafes, cars, etc. Other speakers in the background who mask the speech of interest Target speakers who turn on their music players or their televisions, as they begin to speak, especially when they suspect they are being monitored, in order to mask their speech. The loud music or background noise drowns out the words or makes the speech of the speakers hard to decipher and transcribe. 3. Research Questions Is it possible to reduce or remove: I - interfering music from non- contemporaneous reference material and to bring the voice of the speaker to the forefront? II- background noises, and speech of other speakers, music, etc. from contemporaneous recordings made in the same acoustic environment to bring the voice of the main speaker to the forefront? 4. Example (1,2): Car or Hotel Room Hotel RoomIn a Car Noise sources: Radio, television, music player Noise sources: road noise, car radio, other passengers 5. Example (3): Pub/Hall with Music Noise Sources: Television, Jukebox, Radio, Bar Noise, Other Speakers 6. Research Question (I) Is it possible to reduce or remove interfering music from non-contemporaneous reference material and to bring the voice of the speaker to the forefront? (Alexander and Forth, 2011) 7. Why is this difficult ? Is it possible to reduce or remove interfering music and to bring the voice of the speaker to the forefront? Straightforward subtraction of the audio will not remove the music as the effects of the room are not considered Cancellation is sensitive to clipping and compression. Has often to be applied on a single channel of audio (without simultaneous reference recordings). The exact song that is playing has to be identified and perfectly time-aligned time and labour intensive. 8. Reducing Background Music Tasks involved: Identifying the music/song being played Aligning the tracks to the exact moment in time, within the file being analysed, that the song or music begins Applying a noise- and distortion-robust echo cancellation algorithm to remove or reduce the music while mostly leaving the target speech intact. 9. Automatic Music Identification Commercial applications of acoustic fingerprinting are in areas of identifying tunes, songs, videos, advertisements and radio broadcasts and anti-piracy initiatives. Recent proliferation of music identification systems such as Shazam. A short segment of audio (noisy, distorted or otherwise poor) is sent through to an internet-based recognition server for identification. The server compares feature of this recording to a pre-indexed database of songs. It selects the most probable candidate(s) for the song. 10. Noise-Robust Audio Fingerprinting Query audio 5 10 15 20 25 0 1000 2000 3000 4000 Match: 1-05 The Road To Hell (Part 2) at 179.744 sec 180 185 190 195 200 205 0 1000 2000 3000 4000 Attributes for a fingerprint [Wang (2003)] Temporally localized Translation invariant Robust Sufficiently Entropic Spectral peak pairs are thus temporally localized, robust to noise and transmission distortions 11. Landmark-based Audio Fingerprinting Algorithm (1) Peaks chosen based having higher energy than neighbours Spectrogram is reduced into a constellation map containing spectral peaks. Pairs of peaks selected as landmark hashes that provide reference anchor points in time and frequency. Landmark hash extraction is performed on query audio. 12. Landmark-based Audio Fingerprinting Algorithm (2) t Query Audio Reference audio containing music or noise Time of match (t) Landmark hashes Matching hashes Constellation maps are then compared to obtain the position in time when some of the hashes match, between the query and reference audio. The file with the largest number of hash matches is selected as the reference audio file. An accurate estimate of the time of match is also returned by this algorithm. 13. Query audio 5 10 15 20 25 0 1000 2000 3000 4000 Match: 1-05 The Road To Hell (Part 2) at 179.744 sec 180 185 190 195 200 205 0 1000 2000 3000 4000 Landmark-based Audio Fingerprinting Algorithm (3) Ellis (2009) Robust Landmark-Based Audio Fingerprinting 14. Result Example - Time Domain 0 0.5 1 1.5 2 2.5 3 3.5 4.0 -0.5 0 0.5 Original Signal (Speech and Music) Time (s) 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -0.5 0 0.5 Identified Music Signal Time (s) 1 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -0.5 0 0.5 Resulting Speech (Original - Music) Time (s) Marked reduction in the noise floor 15. Result Example- Frequency Domain 16. Echo Cancellation (1) Echo cancellation suffers from a similar problem playback from the speakers and simultaneous recordings from the microphones the playback should not seep in to the recording in the microphone An acoustic echo canceller could provide a good solution to the problem Echo cancellation algorithms are generally LMS (Least Mean Square- based) either time domain or frequency domain approaches can be used In this application we use an echo canceller software module (compliant with ITU-T G.167, G.168) specifications using Intel Performance Primitives (IPP) library and the DAC CARDINAL. 17. LMS-based Echo Cancellation Speech + residual noise/music (S+N N) (S+N) Speech + noise/music (N) Identified time-aligned noise/music + - Electronic Response estimate (N) Residual 18. LMS / NLMS Coefficient Update , hn (i + 1) = hn (i) + hn (i) Each FIR coefficient h, index n, updated each sample interval i as follows: Update increment, hn (i), computed by LMS algorithm as follows: hn (i) = e (i) x (i - n) NLMS uses a slightly different value, as follows: hn (i) = e (i) x (i - n) where is the specified value (or adapt rate), scaled inversely to the average input signal power 19. Electronic Response Estimate FIR filter coefficients represent an electronic simulation of the rooms acoustical environment Filter must have a sufficient number of taps, N, to not only account for direct acoustic path (A), but also the longest significant reverberation path (B) At 16000 Hz sample rate, required N for example at left would be 0.070s * 16000/s = 1120 taps We typically estimate the minimum required filter length in milliseconds as 5 times largest dimension of the room in feet A Direct Path (13) B Longest significant path (70) Sound: 1 ft ~ 1 msec 15 20. Time Alignment Drift If there is a speed differential between the primary and reference tracks, the time alignment will drift as the processing progresses This can be observed in the FIR coefficient response as a movement of the big spike (the large coefficient associated with the direct path signal correlation), either to the right or the left If drift is significantly fast (e.g. more than 1-2 coefficients every 5-10 seconds), the LMS algorithm will never be able to converge the FIR coefficients to an optimal solution Also, should the spike drift beyond either the beginning or the end of the filter, all cancellation will be lost 21. Research Question (II) Is it possible to reduce or remove, from contemporaneous recordings made in the same acoustic environment, interfering music, background noises, and speech of other speakers, to bring the voice of the main speaker to the forefront? Will having two microphones in the same environment allow for effective cancellation? 22. Applying Audio Fingerprinting to Background Noise Having two microphones in the same acoustic environment perfectly time aligned can greatly help bringing out the voice of one speaker over the other Rarely happens in practise Aligning noise is a more difficult problem as sufficient spectral peaks may not be available in both recordings. Applying a less stringent criteria for matching, we can time- align audio from the two independent recorders in the same acoustic environment accurately. 23. Applications to Noise Identification Speech + residual noise/music (S+N N) (S+N) Speech + noise/music (N) Identified time-aligned noise/music + - Electronic Response estimate (N) Residual 24. Scenarios Scenario 1: Two independent recordings using two smartphones in the same acoustic environment Scenario 2: Two fixed microphones in the same acoustic environment Scenario 3: White noise interference 25. Scenario 1: Two independent recordings using two smartphones Two mobile phones: an iPhone 4S and an iPhone 3GS, were used to record a conversation between two speakers in the same acoustic environment. Two independent devices with not synchronized to each other) in any way. Smaller number of hashes observed -sufficient for time alignment Queried test audio (iPhone 4S) matched and time-aligned against a reference recording (iPhone 3GS) 26. Scenario 2: Two fixed microphones in the same acoustic environment Interfering noise was a television broadcast (2 speakers in a room) Relatively small number of matching hashes as compared with the music Land-marking experiments -> sufficient matches to time-align the two files correctly 27. Scenario 3: White Noise Interference 0 10 20 30 40 50 60 -1 0 1 Time (s) Original speech and white noise 0 10 20 30 40 50 60 -1 0 1 Time (s) Identified and aligned reference white noise 0 10 20 30 40 50 60 -1 0 1 Time (s) Resulting speech (Original - white noise) White noise as the interfering source. Exceedingly difficult to find any distinctive spectral peaks The number of matching hashes was significantly less than observed with either music or regular noise However, we were able to identify a very small number of matching hashes that were sufficient to allow time-alignment. Reference cancellation applied using this time-alignment showed significant improvement in intelligibility. 28. Limitations This method is not applicable to to Badly clipped recordings Compressed recordings Recordings where there is a drift or stretch between the playback time of the music (more applicable to analogue recordings) Note: What is extracted may still not be sufficient quality for forensic voice comparison 29. Conclusions A combination of audio-fingerprinting and echo cancellation can be used to reduce the effect of interfering radio and television noises. This approach could be extended to non-music speech sources by using two independent recordings in the same recording environment A significant improvement in the intelligibility is obtained which could benefit forensic audio enhancement and transcription. 30. References Avery Wang "An Industrial-Strength Audio Search Algorithm", Proc. 2003 ISMIR International Symposium on Music Information Retrieval, Baltimore, MD, Oct. 2003. J. Benesty, D. Morgan and M. Sondhi, (1997) A better understanding and an improved solution to the problems of stereophonic acoustic echo cancellation, Proc. ICASSP,97, 303 D. P. W. Ellis. (2009) Robust Landmark-Based Audio Fingerprinting. http://labrosa.ee.columbia.edu/matlab/fingerprint/ A. Alexander and O. Forth (2011) 'No, thank you, for the music': An application of audio fingerprinting and automatic music signal cancellation for forensic audio enhancement, International Association of Forensic Phonetics and Acoustics Conference 2011, Vienna, Austria, July 2011