Method for processing audio data into a condensed version

• US 20080221876A1
• Filed: 03/08/2007
• Published: 09/11/2008
• Est. Priority Date: 03/08/2007
• Status: Abandoned Application

• Current Assignee: Universitat Fur Musik Und Darstellende Kunst
• Original Assignee: Universitat Fur Musik Und Darstellende Kunst
• Inventors: Holdrich, Robert
• Application Number:
  US11/715,766
• Publication Number:
  US 20080221876A1
• Time in Patent Office: Days
• US Class Current: 704/201
• CPC Class Codes: G10L 21/04 Time compression or expansion
• CPC Class Codes: G11B 20/00007 Time or data compression or expansion audio compression based on psychoacoustics G10L19/00; data processing for reproducing audio data at different playback speeds G10L21/04; video compression H04N19/00; data compression per se H03M7/30
• CPC Class Codes: G11B 2020/00014 the compressed signal being an audio signal

Abstract

Recorded audio data is compressed to obtain a condensed version, by first selecting a number of subsequent non-overlapping segments of the audio data, then reducing each segment by temporal compression and combining the reduced segments into a shortened version which can be output. The temporal compression may be made with a local compression factor which varies between the segments. The segmenting may be chosen based on an innovation signal derived from the audio data itself to indicate a content change rate in the audio data.

Claims

1. A method for processing audio data contained in a recording to obtain a shortened audibly presentable version, comprising:

selecting a number of subsequent non-overlapping segments of the audio data;

reducing each segment by a temporal compression; and

combining the segments thus reduced.

2. The method of claim 1, wherein the temporal compression is made with a time-variant compression factor which varies between the segments.

3. The method of claim 1, wherein selecting of segments of the audio data comprises:

deriving an innovation signal from the audio data, said innovation signal representing a quantity indicating a content change rate in the audio data;

determining time points of maxima of said innovation signal;

selecting segments respectively containing said time points;

reducing said time points by respective time displacements; and

placing segment onsets at time points thus reduced.

4. The method of claim 3, wherein starting from an audio data signal s1(n) the calculation of the innovation signal comprises:

deriving a non-linear quantity y(n)=s1(n)²−

s1(n−

1)·

s1(n+1);

averaging said non-linear quantity with a smoothing function Aν

to obtain an averaged quantity A(n)=Aν

[y(n)]; and

utilizing said averaged quantity as innovation signal Inno(n).

5. The method of claim 3, wherein starting from an audio data signal s1(n) the calculation of the innovation signal comprises:

deriving a non-linear quantity y(n)=s1(n)²−

s1(n−

1)·

s1(n+1);

averaging said non-linear quantity with a smoothing function Aν

to obtain an averaged quantity A(n)=Aν

[y(n)]; and

combining said averaged quantity with its past values A(n−

m) to calculate an innovation signal Inno(n)=A(n)²−

A(n)·

A(n−

m).

6. The method of claim 3, wherein the calculation of the innovation signal comprises:

dividing an audio data signal into a number of frequency band signals;

bandpass filtering the frequency band signals;

calculating a moving average of an instantaneous power of the signals thus filtered using a smoothing function Aν

;

combining the signals thus obtained into a multidimensional power vector P(n); and

calculating a distance function between the actual and a past value of said power vector to derive the innovation signal, Inno(n)=dist[P(n)−

P(n−

m)].

7. The method of claim 3, wherein the calculation of the innovation signal comprises:

dividing an audio data signal into a number of frequency band signals;

calculating a corresponding number of secondary signals from the frequency band signals using at least one of the following methods;

filtering the signal, smoothing the signal, and/or calculation of a local polynomial from the signal;

combining the secondary signals into a multidimensional power vector P(n); and

calculating a distance function between the actual and a past value of said power vector to derive the innovation signal, Inno(n)=dist[P(n)−

P(n−

m)].

8. The method of claim 3, wherein the calculation of the innovation signal comprises:

segmenting the audio data in non-overlapping segments;

calculating a meta-feature vector F(l) from each of said segments;

performing a k-mean clustering of the meta-feature vectors thus obtained; and

calculating a marker signal for each segment by assigning a positive value whenever the meta-feature vector is in a cluster different from the cluster of the previous segment, and a zero value otherwise, to obtain the innovation signal.

9. The method of claim 8, wherein the k-mean clustering is done for G different values of the number k_g of clusters, with g=1, . . . , G, obtaining G marker signals for each segment, and the innovation signal is calculated by averaging a superposition of said marker signals, using a smoothing function Aν

, to obtain the innovation signal, Inno(l)=Aν

(Σ

_gMark_g(l)).

10. The method of claim 9, wherein the calculation of the G marker signals is done using

Mark_g(l)=h(k_g) if F(l) and F(l−

1) are in different clusters

0 otherwise

with an monotonically decreasing function h.

11. The method of claim 8, wherein the calculation of the meta-feature vectors comprises dividing the segments of the audio data into subsegments,

calculating feature vectors for said subsegments;

calculating distribution parameters of said feature vectors; and

combining said distribution parameters into a meta-feature vector.

12. The method of claim 1, wherein the step of segmenting the audio data is based on non-audio data contained in the recording and synchronous to the audio data, wherein segment onset are placed at time markers present in said non-audio data.

13. The method of claim 1, wherein the step of combining the reduced segments is done in chronological order with regard to their original position in the audio date, choosing either a forward order or a reverse order.

14. The method of claim 1, wherein the step of combining the reduced segments comprises superposition of segments.

15. The method of claim 14, wherein the superposition of segments is comprises staggered superposing, wherein the segments start at successive start times and each segment after a first segment has a start time within the duration of a respective previous segment.

16. A method for processing audio data to obtain a graphically presentable version, comprising:

deriving an innovation signal from the audio data, said innovation signal representing a quantity indicating a content change rate in the audio data;

determining time points of maxima of said analysis signal;

placing segment boundaries at time points thus determined; and

displaying the segments thus defined in a linear sequence of faces of varying graphical rendition.