Systems and methods for detecting musical features in audio content
View Patent ↗Systems and methods for identifying musical features in audio content are presented. Audio content information may be obtained from a digital audio file, the information providing a duration for playback of the audio content and a representation of sound frequencies associated with various moments throughout the duration of the audio content. Sound frequencies associated with one or more of the moments throughout the duration of the audio content may be identified, and characteristics or patterns of the identified sound frequencies may be recognized as being indicative of one or more musical features (e.g., parts, phrases, hits, bars, onbeats, beats, quavers, semiquavers, etc.). Some implementations of the present technology define display objects for display on a digital display, the display objects provided with visual features in an arrangement that distinguishes one musical feature from another across the duration of the audio content.
1. A system for identifying musical features in digital audio content, comprising:
one or more physical computer processors configured by computer readable instructions to:
obtain a digital audio file, the digital audio file including information representing audio content, the information providing a duration for playback of the audio content and a representation of sound frequencies associated with one or more moments in the audio content;
identify one or more sound frequencies associated with a first moment in the duration of the audio content;
identify one or more sound frequencies associated with a second moment in the duration of the audio content;
identify one or more frequency characteristics associated with the first moment based on at least one of the one or more sound frequencies associated with the first moment and at least one of the one or more sound frequencies associated with the second moment;
identify one or more musical features associated with the first moment based on the one or more identified frequency characteristics, wherein the one or more musical features include one or more of a phrase, a drop, a hit, a bar, an onbeat, a beat, a quaver, and/or a semiquaver;
identify a transition in the audio content from a first part to a second part, the transition identified at a third moment in the duration of the audio content; and
adjust the identification of the transition from the third moment to a fourth moment in the duration of the audio content based on at least one of the one or more identified musical features.
2. The system of claim 1 , wherein the one or more of the frequency characteristics include amplitude associated with the first moment.
3. The system of claim 1 , wherein the identification of the transition is based on using a Hidden Markov Model.
4. The system of claim 1 , wherein the identification of the one or more musical features is based on a match between one or more of the identified frequency characteristics and a predetermined frequency pattern template corresponding to a particular musical feature.
5. The system of claim 1 , wherein the identification of the transition is adjusted to the fourth moment to occur between two of the one or more identified musical features.
6. The system of claim 1 , wherein the identification of the transition is adjusted further based on a first duration of the first part and/or a second duration of the second part being shorter than a threshold duration.
7. The system of claim 1 , wherein the identification of the transition is adjusted to the fourth moment to coincide with one of the one or more identified musical features.
8. The system of claim 7 , wherein the one of the one or more identified musical features is selected for the adjustment of the identification of the transition based on a hierarchy of musical features, the hierarchy of musical features including an order of different types of musical features from a highest priority to a lowest priority.
9. The system of claim 8 , wherein the one of the one or more identified musical features has the highest priority among the one or more identified musical features.
10. The system of claim 8 , wherein the order includes, from the highest priority to the lowest priority, a phrase musical feature, a drop musical feature, a hit musical feature, a bar musical feature, an onbeat musical feature, a beat musical feature, a quaver musical feature, and a semiquaver musical feature.
11. A method for identifying musical features in digital audio content, the method comprising the steps of:
obtaining a digital audio file, the digital audio file including information representing audio content, the information providing a duration for playback of the audio content and a representation of sound frequencies associated with one or more moments in the audio content;
identifying one or more sound frequencies associated with a first moment in the duration of the audio content;
identifying one or more sound frequencies associated with a second moment in the duration of the audio content;
identifying one or more frequency characteristics associated with the first moment based on at least one of the one or more sound frequencies associated with the first moment and at least one of the one or more sound frequencies associated with the second moment;
identifying one or more musical features associated with the first moment based on the one or more identified frequency characteristics, wherein the one or more musical features include one or more of a phrase, a drop, a hit, a bar, an onbeat, a beat, a quaver, and/or a semiquaver;
identifying a transition in the audio content from a first part to a second part, the transition identified at a third moment in the duration of the audio content; and
adjusting the identification of the transition from the third moment to a fourth moment in the duration of the audio content based on at least one of the one or more identified musical features.
12. The method of claim 11 , wherein the one or more of the frequency characteristics include amplitude associated with the first moment.
13. The method of claim 11 , wherein identifying the transition is based on using a Hidden Markov Model.
14. The method of claim 11 , wherein the identification of the one or more musical features is based on a match between one or more of the identified frequency characteristics and a predetermined frequency pattern template corresponding to a particular musical feature.
15. The method of claim 11 , wherein the identification of the transition is adjusted to the fourth moment to occur between two of the one or more identified musical features.
16. The method of claim 11 , wherein the identification of the transition is adjusted further based on a first duration of the first part and/or a second duration of the second part being shorter than a threshold duration.
17. The method of claim 11 , wherein the identification of the transition is adjusted to the fourth moment to coincide with one of the one or more identified musical features.
18. The method of claim 17 , wherein the one of the one or more identified musical features is selected for the adjustment of the identification of the transition based on a hierarchy of musical features, the hierarchy of musical features including an order of different types of musical features from a highest priority to a lowest priority.
19. The method of claim 18 , wherein the one of the one or more identified musical features has the highest priority among the one or more identified musical features.
20. The method of claim 18 , wherein the order includes, from the highest priority to the lowest priority, a phrase musical feature, a drop musical feature, a hit musical feature, a bar musical feature, an onbeat musical feature, a beat musical feature, a quaver musical feature, and a semiquaver musical feature.