Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-speech alignment, lyrics alignment remains highly challenging, despite many attempts to combine numerous sub-modules including vocal separation and detection in an effort to break down the problem. Furthermore, training required fine-grained annotations to be available in some form. Here, we present a novel system based on a modified Wave-U-Net architecture, which predicts character probabilities directly from raw audio using learnt multi-scale representations of the various signal components. There are no sub-modules whose interdependencies need to be optimized. Our training procedure is designed to work with weak, line-level annotations available in the real world. With a mean alignment error of 0.35s on a standard dataset our system outperforms the state-of-the-art by an order of magnitude.
Several approaches make additional assumptions to further simplify the problem. For example, the method presented in @cite_18 assumes that chord labels are attached to the lyrics and exploits them during the alignment process. Other approaches assume that the lyrics are pre-aligned at a line or phrase level so that the method only needs to refine the alignment within these sections @cite_25 @cite_11 @cite_10 . Since music often contains repeated segments, some methods additionally analyze and compare the musical structure in a recording and in corresponding lyrics @cite_3 @cite_27 .
@cite_18 @cite_3 @cite_27 @cite_10 @cite_25 @cite_11
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Aligning lyrics to audio has a wide range of applications such as the automatic generation of karaoke scores, song-browsing by lyrics, and the generation of audio thumbnails. Existing methods are restricted to using only lyrics and match them to phoneme features extracted from the audio (usually mel-frequency cepstral coefficients). Our novel idea is to integrate the textual chord information provided in the paired chords-lyrics format known from song books and Internet sites into the inference procedure. We propose two novel methods that implement this idea: First, assuming that all chords of a song are known, we extend a hidden Markov model (HMM) framework by including chord changes in the Markov chain and an additional audio feature (chroma) in the emission vector; second, for the more realistic case in which some chord information is missing, we present a method that recovers the missing chord information by exploiting repetition in the song. We conducted experiments with five changing parameters and show that with accuracies of 87.5 and 76.7 , respectively, both methods perform better than the baseline with statistical significance. We introduce the new accompaniment interface Song Prompter, which uses the automatically aligned lyrics to guide musicians through a song. It demonstrates that the automatic alignment is accurate enough to be used in a musical performance. We present a framework to synchronize pop music to corresponding text lyric. We refine line level alignment achievable by existing work to syllabic level by using a dynamic programming process. Our main contribution is using music knowledge to constrain the dynamic programming search. This is done by modeling (1) non-uniform note length distribution and (2) a note length distribution for each section type (for example intro, chorus, and bridge). These reduce alignment error by 6.4 and improve time efficiency by a factor of 2.2. We present a prototype that automatically aligns acoustic musical signals with their corresponding textual lyrics, in a manner similar to manually-aligned karaoke. We tackle this problem using a multimodal approach, where the appropriate pairing of audio and text processing helps create a more accurate system. Our audio processing technique uses a combination of top-down and bottom-up approaches, combining the strength of low-level audio features and high-level musical knowledge to determine the hierarchical rhythm structure, singing voice and chorus sections in the musical audio. Text processing is also employed to approximate the length of the sung passages using the textual lyrics. Results show an average error of less than one bar for per-line alignment of the lyrics on a test bed of 20 songs (sampled from CD audio and carefully selected for variety). We perform holistic and per-component testing and analysis and outline steps for further development. The massive amount of digital music data available necessitates automated methods for processing, classifying and organizing large volumes of songs. As music discovery and interactive music applications become commonplace, the ability to synchronize lyric text information with an audio recording has gained interest. This paper presents an approach for lyric-audio alignment by comparing synthesized speech with a vocal track removed from an instrument mixture using source separation. We take a hierarchical approach to solve the problem, assuming a set of paragraph-music segment pairs is given and focus on within-segment lyric alignment at the word level. A synthesized speech signal is generated to reflect the properties of the music signal by controlling the speech rate and gender. Dynamic time warping finds the shortest path between the synthesized speech and separated vocal. The resulting path is used to calculate the timestamps of words in the original signal. The system results in approximately half a second of misalignment error on average. Finally, we discuss the challenges and suggest improvements to the method.