# Conditional Generation of Bass Guitar Tablature for Guitar Accompaniment in Western Popular Music

Olivier Anoufa, Alexandre D'Hooge, Ken Déguernel

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Olivier Anoufa, Alexandre D'Hooge, Ken Déguernel. Conditional Generation of Bass Guitar Tablature for Guitar Accompaniment in Western Popular Music. AI Music Creativity, 2025, Bruxelles, Belgium. hal-05216640

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Distributed under a Creative Commons Attribution 4.0 International License
Conditional Generation of Bass Guitar Tablature for
Guitar Accompaniment in Western Popular Music

Olivier Anoufa

Alexandre D'Hooge

Ken Déguernel

Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRISTAL, F-59000 Lille, France
[firstname]. [lastname]@algomus.fr

Abstract

Bass guitar plays a preeminent role in Western Popular Music, having to convey both rhythmic and harmonic information. In this paper, we propose a transformer-based decoder model suggesting idiomatic bass guitar tablatures, conditioned on a rhythm guitar track. Using the DadaGP dataset and its tokenization scheme, we introduce a pipeline to identify rhythm guitar tracks and extract them along with the matching bass guitar tablature. The model is trained on over 100'000 16-bars rhythm guitar excerpts and generate relevant bass lines suggestions. We qualitatively analyze the generated bass tablatures and identify the key qualities and drawbacks of the system, like its ability to follow harmony, and rhythm whilst generating performable and natural hand movements for bass players, but with a tendency to copy the rhythm guitar too closely. All code and pretrained models are made publicly available, along with a demonstration tool.

# 1 Introduction

In Western Popular Music (WPM), the bass guitar provides the harmonic foundation of a tune whilst driving the rhythm section (Hove et al., 2014). Composing engaging bass lines is therefore an important aspect of song writing. A user study by Bacot et al. (2025) revealed a significant demand among guitarists for accompaniment generation tools. More precisely, several guitarists expressed during semi-structured interviews the desire for a tool which, given a guitar part they composed themselves, would generate bass lines and drum parts. They explain that, during the composition process and whilst preparing their demo, guitarists often resort to writing basic bass and drum tracks to accompany their compositions, due to a limited familiarity with these instruments. In this paper, we consider the task of composing a bassline from a rhythm guitar tablature alone, which can be typical for a singer-songwriter guitar player. More specifically, we focus on conditioned symbolic music for bass guitar tablatures within the context of WPM.

Tablatures are used as a notation system for fretted string instruments. They are an intuitive alternative to staff notation, explicitly linking symbols to physical gesture on the instrument, such as pressing specific frets or strings. Figure 1 shows an example of tablatures of bass and guitar. For each instrument, each line correspond to a string, and then numbers shows which fret should be played on that string. With the rise of internet forums and music sharing platforms, tablature has become the primary notation format for guitarists to write and share their compositions, as well as to learn new songs (Bacot et al., 2025). First shared in ASCII format for its convenience, tablatures were later digitized and standardized in formats like *GuitarPro*<fnref n="1" />, further extending their use for learning, practicing, and composing in a variety of styles of Western classical music and WPM (Sarmento et al., 2021). As a majority of guitarists learn many aspects of the songs they play by listening to them (Green, 2001), tablatures are a prescriptive way to teach how to play a song that is already known by the musician.

<footnote n="1">¹https://www.guitar-pro.com, accessed in June 2025.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Tablatures for rhythm guitar (top) and bass (bottom). Excerpt from *Living in the Past* by Jethro Tull.</figcaption></figure>

While the role bass plays in WPM is usually more than a mere accompaniment, it is rarely the main instrument of a song, and generating bass tablature is thus akin to the Music Information Retrieval (MIR) task of accompaniment generation. Suggesting bass tracks is already studied in the audio domain for WPM in (Grachten et al., 2020; Pasini et al., 2024). This approach is generalized to any accompaniment tracks of a lead melody in Nistal et al. (2024). Accompaniment generation has also been tackled in the symbolic domain, using formats like MIDI and conditioning on sung melodies (Ren et al., 2020; Huang et al., 2018). Focusing specifically on drums, (Makris et al., 2022; Dahale et al., 2022) generate MIDI drum tracks conditioned on all other instruments, with the addition of improvised fills for the latter. Conversely, it is possible to take a single track as input and generate multiple others (Dong et al., 2018), or even generate any number of output tracks from any number of input tracks (Ens and Pasquier, 2020). The previously mentioned approaches, while efficient, rarely consider bass guitar or, if they do, use the MIDI file format. This representation is not a score and is not designed for performance nor does it include idiomatic playing techniques information.

To the best of our knowledge, there is no published work specifically on the generation of bass guitar tablatures. However, there have been multiple works studying the conditional generation of guitar tablatures. In (McVicar et al., 2014b,a, 2015), the authors generate lead and rhythm guitar tablatures from an input chord progression by automatically selecting rhythm and fretboard positions from a tablature database. An existing chord progression is also used in (Sakai et al., 2024), where a lead sheet is converted to a fingerstyle guitar tablature. Transformer models were also applied to the task of tablature generation. Still for fingerstyle guitar, (Chen et al., 2020) use grooving patterns to drive the model's generation towards desired rhythms. Finally, in (Sarmento et al., 2023a,b), the Transformer-XL model and DadaGP dataset from (Sarmento et al., 2021) are used for conditional generation using priming sequences in the former, or to imitate the playing style of famous guitar players in the latter.

While models based on (Sarmento et al., 2021) can generate bass tablatures, none of the proposed models allow to conditionally generate fitting bass guitar parts. Besides, preliminary experiments with the GTR-CTRL model (Sarmento et al., 2023a) showed that control tokens in the prompt were not enough in driving the model towards generating bass tracks, as other instruments would eventually appear. That approach is also restricted to free generation so the bass tracks can never really match an existing guitar tablature. Those experiments confirmed the need for a custom model and method for generating bass guitar tablatures.

As a first step towards this goal, the contributions of this paper are as follows: i) Isolating bass guitar and rhythm guitar tracks from the DadaGP dataset (section 2); ii) Adapting an existing conditional accompaniment generation architecture to bass guitar tablature (section 3); iii) A qualitative analysis of the generated content, with future steps to improve bass tablature generation (section 4).

The code for this paper is shared publicly under a GPL3 license: https://github.com/adhooge/BassTablatureGeneration/

## 2 Data Preparation

Bass guitar, especially in tablature format, is particularly rare in datasets available to the MIR community. One noteworthy exception is the DadaGP dataset, publicly available to researchers since Sarmento et al. (2021). This dataset contains over 26 000 songs of various styles of WPM, with a
<figure><img src="image_4.png" /><figcaption>Figure 2: Example measure of a tablature with two distorted guitars and a bass, and the corresponding tokens (right) from the DadaGP dataset Sarmento et al. (2021). Excerpt from *Pilgrim*, by Arch Enemy.</figcaption></figure>

bias towards rock and metal music. The files are amateur transcriptions of WPM songs and can include multiple tracks for the different instruments encountered. While guitar is the most represented instrument, many songs also contain a bass guitar track notated in tablature format. The dataset is made of *GuitarPro* files, but an alternative to this proprietary format is provided as *tokens* files. We use those preprocessed files, and present the tokenization format and required preprocessing steps in the following section.

The data preparation pipeline consists of three stages: (1) parsing and preprocessing the DadaGP token files to extract bass guitar tablature, (2) identifying rhythm guitar tracks to serve as conditioning input, and (3) segmenting the data into manageable sequences for model training.

## 2.1 Tokenization and Preprocessing

The DadaGP tokenization format adopts an event-based approach (Le et al., 2025), similar to other music generation models, by representing musical events as discrete tokens. A measure and its corresponding tokens is shown Figure 2. All token files start with *metadata* tokens that state the artist (if known), the starting tempo of the song and an optional *downtuning*. This last element refers to the distance in semitones between the current string pitches and the *standard tuning*: (E₁, A₁, D₂, G₂) for a 4-strings bass. The musical content is then encoded with *new\_measure* tokens and notes are written in the form of `<instrument>:note:<string>:<fret>` (for example *bass:note:s4:f7*) as a way to directly encode tablature notation. Each note token can be followed by note effects token (*nfx*) that represent playing techniques that are also denoted in the tablatures (Josel and Tsao, 2014). Finally, unlike REMI encoding where the note duration is provided right after the note token (Huang and Yang, 2020), the DadaGP tokenization uses *wait* tokens that indicates the number of ticks to wait before playing the next note. This approach allows for shorter token sequences since a single *wait* token may apply to multiple notes, while also being more robust than MIDI-like encodings since every new note mutes the previous one (on the same instrument), suppressing the risk of missing *NOTE_OFF* tokens.

To prepare the data for training, we follow a structured preprocessing pipeline that ensures clean and well-formatted input sequences. The first step involves retrieving and cleaning the DadaGP dataset,
<figure><img src="image_5.png" /><figcaption>Figure 3: Example of an extraction of bass guitar tokens from *Last Nite* by The Strokes.</figcaption></figure>

extracting tokens specific to selected instruments while preserving relevant note effect tokens and metadata. As we want to generate bass guitar tablatures conditioned on rhythm guitar, we extract tokens specific to those instruments. Preliminary experiments showed that reprocessing all GuitarPro files one track at a time would have required unnecessarily high computational power and time. To perform the extraction of instrument-specific tokens, we thus filter tokens from the already existing files. We leverage the fact that tokens of a given instrument start with the instrument name followed by a colon, e.g., **bass**: for the bass guitar, to automatically detect such tokens. However, we must be careful not to miss the potential note effect tokens that are not instrument-specific, but come right after the token they are related to. After extracting those tokens and the general tokens (metadata tokens and wait tokens), we sum the potential consecutive wait tokens that were previously separated by notes from instruments that are no longer present (Figure 3). We end up with 14 480 bass track token files. Notes from all other instruments are removed, as well as **bfx** (bar effects, for tempo changes for instance) tokens to reduce the sequences' complexity. Those **bfx** tokens are unrelated to the meter and have thus no impact on the final rhythm conversion of the detokenizer algorithm. We consider that removing **bfx** tokens is a minor simplification, as modifications like tempo changes can always be added in a post-processing step if the guitarist needs them.

## 2.2 Rhythm guitar identification

The method presented above allows us to extract tokens for any instrument in the DadaGP dataset. WPM typically differentiates guitar roles: lead guitars handle melodic lines and solos, while rhythm guitars provide the core harmonic and rhythmic structures (Régnier et al., 2021). While the bass guitar typically establishes the low-end foundation and root notes of the chord progression, the rhythm guitar complements this by providing the full harmonic voicings and rhythmic texture in a higher frequency range. This characteristic makes rhythm guitar therefore particularly relevant for conditioning bass generation tasks and are thus used as the input of our system. Drum tracks could also provide the bass generation model with a strong rhythmic foundation but, because our objective is to assist guitarist composers who do not play the drums or bass, assuming a pre-existing drum track seems unrealistic. We thus decide to condition the generation of bass tablatures on rhythm guitar tablatures only.
<figure><img src="image_6.png" /><figcaption>Figure 4: Example of rhythm guitar selection after applying the method of Régnier et al. (2021)</figcaption></figure>

To identify rhythm guitar parts in the DadaGP dataset, we implemented the method proposed in (Régnier et al., 2021), which uses features describing notes and chords at the bar level, along with corresponding tablatures. Because each guitar track in a song can have a rhythm guitar role at some point, we select the track that has the most bars classified as rhythm guitar and extract its tokens with the same procedure as for the bass, described before. Implementing this method required adapting the original code to the DadaGP dataset. Significant effort was spent mapping the identified rhythm guitar parts to their respective names in the DadaGP files, as the dataset renames instruments, whereas the identification tool relies on GuitarPro part names. The original model is trained and tested on a proprietary dataset, but manual verification of the tracks identified as rhythm guitar in DadaGP showed that the model generalized well to this new dataset. It was expected to some extent, since the original proprietary dataset used in Régnier et al. (2021) also consists mostly of WPM songs.

Figure 4 shows an example of the selection of the rhythm guitar part. After retrieving the instrument with the highest number of rhythmic bars over the song, we generate a column `is_track_rhythmic` that is filled with 1 (or True) for the rhythm guitar and 0 for the other instruments.

## 2.3 Sequence extraction and filtering

To avoid sequences that are too long and that could not be processed by our model, we chose to extract samples of 16 measures from the songs. We extract sequences with a sliding window of 8 measures, which allows for some overlap between the sequences. This will help the model learn transitions between measure groups, and observe the various possible contexts around a given sequence.

A maximum threshold of 800 tokens, and a minimum threshold of 50 tokens were also set to avoid training on sequences that are either too long or that almost do not contain rhythm guitar or bass. Manual verification showed that no artists were discarded when applying those thresholds. Figure 5 shows the distribution of sequence lengths (i.e. the amount of tokens in the 16 bar sequences) in the training dataset. The majority of the sequences are between 200 and 400 tokens long for the rhythm guitar and between 100 and 300 tokens long for the bass. Those distributions illustrate how the number of tokens can vary even though the sequences always contain 16 measures. This can be due to the use of chords or short notes, that increase the possible number of tokens in a sequence. The final dictionary of sequences is finally split into training, validation and test sets with proportions 0.8, 0.1 and 0.1 respectively. We ensure that all sequences from a same file are in the same subset, to limit data-leakage possibilities. We extracted 118 167 sequences in total, which leads to 94 533 sequences in the training set and 11 817 in both the validation and test sets.

# 3 Method

## 3.1 Model

We base our work on the conditional drum generation model of (Makris et al., 2022). It is based on a BiLSTM to first encode the input track (rhythm guitar in our case) before feeding it to a Transformer Decoder with Relative Global Attention (Huang and Yang, 2020) that generates the output tokens (the bass guitar track).
<figure><img src="image_7.png" /><figcaption>Figure 5: Distribution of sequence lengths (in number of tokens) in the training dataset</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 6: Adapted implementation of the model from Makris et al. (2022).</figcaption></figure>

Figure 6 shows the model adapted to our task. Unlike the original implementation that uses a Compound-Word architecture, we merge all encoders and process the input tokens sequentially with a single encoder network to use the DadaGP tokenization format directly. The decoder network also generates tokens in a single sequence.

The Encoder processes rhythm guitar tokens using a bidirectional Long Short-Term Memory (BiLSTM) network, allowing the model to capture dependencies both forward and backward in the sequence. The BiLSTM generates a latent variable *z*, which encodes both rhythmic and structural information from the input sequence. This latent variable serves as a compressed representation of the rhythm guitar part. The Decoder follows an autoregressive approach, predicting each token sequentially. At each time step *t* — 1, the Decoder receives the previously generated token, embedded through a dense layer, along with the latent variable *z* from the Encoder, which provides contextual information. The Decoder is based on a Transformer architecture with self-attention layers, allowing it to model long-range dependencies within the generated sequence. Unlike standard Transformers that rely solely on absolute positional encodings, it incorporates Relative Global Attention to enhance the model’s ability to capture hierarchical musical structures. This mechanism enables the model to account for both global position information and relative relationships between musical events, which is crucial for generating coherent bass lines that align with the rhythm guitar.

Following prior work on relative attention, we set the relative attention window size to half of the total sequence length. This balances local context within measures and global context across phrases, improving the overall consistency of the generated bass lines. The final output of the Transformer Decoder consists of relation-aware self-attention activations $h_t$, which are passed through a Dense layer followed by a softmax activation. Our adapted model consists of approximately 21 million parameters, comparable to other large models such as the Pop Music Transformer, which has around 41 million parameters (Huang and Yang, 2020).
To evaluate our model’s performance during training, we use a loss function based on categorical cross-entropy and an accuracy metric designed for sequence modeling. The loss function measures how well the predicted probability distribution aligns with the true tokens and is defined as:

$$ \mathcal{L} = - \sum_{t=1}^{T} \sum_{k=1}^{K} y_{t,k} \log \hat{y}_{t,k} \qquad (1) $$

where T is the sequence length, K is the vocabulary size, $y_{t,k}$ is the ground-truth token at time step t, and $\hat{y}_{t,k}$ is the predicted probability for token k. The ground-truth token is the token that effectively appears in the original bass guitar sequence at time step t. Our accuracy metric computes the proportion of correctly predicted tokens in a sequence. Both functions incorporate a masking mechanism to handle padding tokens and remove their contribution to the calculations. While the reference bass sequence is by no means the only valid answer in a musical sense, it is used during training as the expected prediction of the model.

**Implementation details** The architecture is the same as (Makris et al., 2022), with only the sizes of the encoding and input/output layers adapted to our problem. The resulting model thus contains 4 attention layers with 8 attention heads, with encoder and decoder embeddings being of size 240 and 192, respectively. The BiLSTM network has 1024 units in total, 512 for each direction.

Training was done on GPU with an early-stopping criterion of 5 epochs without improvement on the validation loss. We used Adam optimizer with the following hyperparameters: $\alpha = 5 \times 10^{-4}$, $\beta_1 = 0.9$, $\beta_2 = 0.98$, $\epsilon = 10^{-9}$, and a batch size of 32.

**Generation parameters** Inference from the model is conducted with a temperature $T = 0.9$ by sampling tokens from the logits converted to probabilities and using them as a multinomial distribution. Inference stops as soon as an <end> token is generated, or when the maximum of 800 tokens is reached.

# 4 Qualitative analysis of generation

As this bass line generation tool is currently a proof-of-concept, this section focuses on a qualitative analysis of generated bass guitar tablatures. While quantitative evaluation methods are valuable for measuring model accuracy in well-defined tasks, they are less suitable for assessing computational creativity tasks where multiple valid outputs may exist (Jordanous, 2011). For instance, edit distance or perplexity metrics only assess if the generation is close to the reference, which does not measure the musical quality of the output. Other high-level metrics might seem attractive, to measure harmonic and rhythmic consistency for instance, but a bass playing “dissonant” notes or diverging from the guitar’s rhythm are not necessarily wrong musical decisions. We thus choose to not implement any automatic quantitative metrics, as we believe they would bring little value to the current evaluation.

The model presented in this paper is designed to help guitarist composers generate idiomatic and musically coherent bass lines given their composed rhythm guitar tablatures. In this context, qualitative analysis provides more informative insights into the musical relevance, performability, as well as the limitations of the generated bass lines (Ritchie, 2019). Analyses were conducted by defining codes and assigning examples to each code. The generations were explored randomly thanks to the demonstration website, and the exploration was stopped once no new codes seemed necessary to reflect the content of the generated tablatures. The final categories were cross-checked by the other authors for further grouping of similar codes. The authors are aware of the limitations and possible biases that can occur from such subjective analyses. Nevertheless, the evaluation was conducted in an inductive way, meaning that codes emerged from the samples, and were later linked with musical knowledge to reflect upon the model’s performance. By making an exploration website publicly available, we leave to readers the opportunity to check the generated samples freely, allowing them to verify our claims.

To support ongoing exploration and evaluation, the code for the tool as well some generation examples from the test set are provided alongside this paper at this link:
<figure><img src="image_9.png" /><figcaption>Figure 7: Generated example 518. Here, the bass plays full 16th notes even though the guitar part is more sparse.</figcaption></figure>

https://github.com/adhooge/BassTablatureGeneration/.

This demonstration tool uses the alphaTab library to display and play tablatures. Please note that the varying audio quality and minor display issues might occur and are not related to the present work.

First, we analyse the harmonic and rhythmic consistency of the generated bass lines with respect to the conditioning guitar part Second, we assess the naturalness of the resulting tablatures in term of bass guitar technique, focusing on physical playability and idiomatic movement across the instrument. Finally, we examine the system's ability to stay stylistically relevant to the guitar part.

Each analysis will refer to some particular examples by their ids. Note that you
can directly access this example by modifying the url of the demo website. For
example, for id 222, you can directly go to

https://adhooge.github.io/BassTablatureGeneration/?id=222.

All ids are also hyperlinked, so you can simply click on them.

## 4.1 Harmonic and rhythmic features

We firstly observe that the generated bass tablatures can *highlight chord tones* even when they are not explicitly played in the guitar part. The bass also tends to *fill voids and rests* with relevant rhythms and notes (ids 518, 1029, 1215, Figure 7). The generated bass parts also can *anticipate* the upcoming guitar chords and play notes that match the upcoming key (899). Notes that are unexpected in the current harmonic context can also be encountered, even without being completely dissonant (1133, 1306). Rhythmically, the model has a *tendency to generate eighth notes*, or sixteenth notes in a filling non-stop fashion (1151). *Less common rhythms* can however be encountered, either following the guitar (873, 26) or diverging from it (172). Nonetheless, it happens that the bass is rhythmically consistent with itself without really matching with the rhythm of the guitar (877). Another limitation is that the bass part can be harmonically and rhythmically consistent but *simplistic* (952). The model might benefit from more varied data where the bass diverges from the guitar in an interesting way.

## 4.2 Gesture and bass guitar idiomaticity

A satisfying observation is that the generated tablatures usually seem to replicate idiomatic bass guitar playing. For example, while the bass tends to replicate what the guitar plays, it plays *single notes rather than chords*, that are harder and less common in bass guitar (121, 126, 949). Playing techniques like dead notes can also be generated (22), as can be slides or hammer-on and pull-off. Another important observation is that the tablatures tend to contain moves perpendicular to the fretboard (209, 752, 1209, Figure 8) which is more common among bass players, notes resonating more fully closer to the beginning of the fretboard. Octave-jumps are also typical bass-guitar playing and can be encountered (534, Figure 9) as well as typical string-jumps (1275). It's also worth noting that the bass is not always playing accompaniment and melodic parts are sometimes generated (917, 961, 1351). Similarly, "transition" notes (1354) or chromaticisms (965), typically encountered in bass guitar parts, are generated.
<figure><img src="image_10.png" /><figcaption>Figure 8: Generated example 209. While the guitar goes up the fretboard, the bass moves perpendicularly.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 9: Generated example 534. Example of idiomatic octave jumps in the bass part.</figcaption></figure>

But the generated content can also be very repetitive (1119, 83, 474, 1067) and while this can happen in actual bass parts, it would be more interesting to generate parts with more variation to better assist guitar players. Finally, the bass can sometimes copy the guitar too much, resulting in difficultly playable tablatures with bends for instance (1167).

## 4.3 Stylistic consistency

A good example of how the model adapts to different musical styles is on metal sub genres. Those styles are very present in the DadaGP dataset, and we observe that the generated bass indeed matches the required style by closely following the rhythm guitar (268, 288, 718, 719, Figure 10). Likewise, rock music (or any subgenre) is usually well reproduced, closely following the guitar part but with additional minor variations (844). Conversely, styles of guitar playing that are less represented in DadaGP leads to inappropriate bass parts (1355), like when more complex chords akin to jazz music are used by the guitar.

When the guitar is more melodic (which can happen even if the track is rhythm guitar overall), the model still manages to suggest a bass track that matches the harmony and the style of the melody (795, 499, 166, 588, Figure 11). However, the bass part can sometimes repeat itself too much and miss mood changes (68), even though it's up to musicians to decide if this unacceptable. Another limitation to the current system is that it sometimes seems to be late (725) or early (84) on following the changes in the way the guitar plays.
<figure><img src="image_12.png" /><figcaption>Figure 10: Generated example 719. This is a fast metal excerpt, the bass follows the guitar closely but reduces chords to single notes.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Figure 11: Generated example 588. Even though the guitar plays a more melodic part, the bass manages to make the underlying chords heard.</figcaption></figure>

# 5 Conclusion and Future Work

In this work, we explored the conditional generation of bass guitar tablatures using a transformer-based model trained on the DadaGP dataset. We implemented a detailed data preprocessing pipeline, including instrument-specific token extraction, rhythm guitar identification, and structured sequence formatting. Our approach enables the generation of bass lines that are harmonically and rhythmically coherent with a given rhythm guitar part. Our results demonstrate that the model effectively captures the structural role of the bass guitar in WPM, successfully aligning with the rhythmic and harmonic elements of the accompaniment. However, we also identified certain limitations, such as the model's tendency to replicate rhythmic patterns excessively and being less idiomatic in styles that are less represented in the dataset.

While our study establishes a strong foundation for conditioned bass tablature generation, further refinements in model design, data representation, and evaluation methods could enhance its practical applicability. One potential enhancement is simplifying the model by removing one of the dense layers in the embedding process, which could reduce complexity without significantly impacting performance. Additionally, experimenting with compound word tokenization as an alternative to the current DadaGP method might yield more meaningful representations and help the model during the training phase. Future work could also experiment with other transformer architectures, like encoder-decoder or decoder-only transformers. To further improve the model's flexibility and idiomaticity across diverse musical styles, we could involve either fine-tuning dedicated sub-models for specific styles or incorporating style descriptor tokens during training to condition the generation on a target style. However, the current version of the DadaGP dataset does not include explicit style annotations (only artist names are provided) limiting our ability to systematically explore this direction at present.
Looking ahead, future work involve integrating this proof-of-concept generation tool into a dedicated tablature composition environment. This integration will enable human-AI co-creative workflows, allowing musicians to interactively explore bassline suggestions and iteratively refine them. Such a setting will support the development of more robust and context-aware generative tools tailored to real-world creative practices. Once integrated, we plan to conduct qualitative user studies involving target users of the composition software. Through interviews and think-aloud protocols, we will assess the tool's practical impact, usability, and creative potential from a user-centred perspective.

## Acknowledgments and Disclosure of Funding

The authors want to thank Pedro Sarmento for his help in using the DadaGP model and dataset in the early phase of this research. The authors also thank Louis Bigo, Dinh-Viet-Toan Le and the reviewers for their feedback.

This work is partly funded under the TABASCO ANR Project (ANR-22-CE38-0001) and model training was conducted on *Mésocentre de l'Université de Lille*.

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