libavcodec/aacpsy.c:133: error: Uninitialized Value
  The value read from barks[_] was never initialized.

libavcodec/aacpsy.c:133:13: 
131.         for (g = 0; g < ctx->num_bands[j]; g++) {
132.             i += ctx->bands[j][g];
133.             coeffs->barks[g] = (barks[i - 1] + prev) / 2.0;
                 ^
134.             prev = barks[i - 1];
135.         }

static av_cold int psy_3gpp_init(FFPsyContext *ctx) {
    Psy3gppContext *pctx;
    float barks[1024];
    int i, j, g, start;
    float prev, minscale, minath;
    ctx->model_priv_data = av_mallocz(sizeof(Psy3gppContext));
    pctx = (Psy3gppContext*) ctx->model_priv_data;
    for (i = 0; i < 1024; i++)
        barks[i] = calc_bark(i * ctx->avctx->sample_rate / 2048.0);
    minath = ath(3410, ATH_ADD);
    for (j = 0; j < 2; j++) {
        Psy3gppCoeffs *coeffs = &pctx->psy_coef[j];
        i = 0;
        prev = 0.0;
        for (g = 0; g < ctx->num_bands[j]; g++) {
            i += ctx->bands[j][g];
            coeffs->barks[g] = (barks[i - 1] + prev) / 2.0;
            prev = barks[i - 1];
        }
        for (g = 0; g < ctx->num_bands[j] - 1; g++) {
            coeffs->spread_low[g] = pow(10.0, -(coeffs->barks[g+1] - coeffs->barks[g]) * PSY_3GPP_SPREAD_LOW);
            coeffs->spread_hi [g] = pow(10.0, -(coeffs->barks[g+1] - coeffs->barks[g]) * PSY_3GPP_SPREAD_HI);
        }
        start = 0;
        for (g = 0; g < ctx->num_bands[j]; g++) {
            minscale = ath(ctx->avctx->sample_rate * start / 1024.0, ATH_ADD);
            for (i = 1; i < ctx->bands[j][g]; i++)
                minscale = FFMIN(minscale, ath(ctx->avctx->sample_rate * (start + i) / 1024.0 / 2.0, ATH_ADD));
            coeffs->ath[g] = minscale - minath;
            start += ctx->bands[j][g];
        }
    }
    pctx->ch = av_mallocz(sizeof(Psy3gppChannel) * ctx->avctx->channels);
    return 0;
}
['static av_cold int psy_3gpp_init(FFPsyContext *ctx) {\n    Psy3gppContext *pctx;\n    float barks[1024];\n    int i, j, g, start;\n    float prev, minscale, minath;\n    ctx->model_priv_data = av_mallocz(sizeof(Psy3gppContext));\n    pctx = (Psy3gppContext*) ctx->model_priv_data;\n    for (i = 0; i < 1024; i++)\n        barks[i] = calc_bark(i * ctx->avctx->sample_rate / 2048.0);\n    minath = ath(3410, ATH_ADD);\n    for (j = 0; j < 2; j++) {\n        Psy3gppCoeffs *coeffs = &pctx->psy_coef[j];\n        i = 0;\n        prev = 0.0;\n        for (g = 0; g < ctx->num_bands[j]; g++) {\n            i += ctx->bands[j][g];\n            coeffs->barks[g] = (barks[i - 1] + prev) / 2.0;\n            prev = barks[i - 1];\n        }\n        for (g = 0; g < ctx->num_bands[j] - 1; g++) {\n            coeffs->spread_low[g] = pow(10.0, -(coeffs->barks[g+1] - coeffs->barks[g]) * PSY_3GPP_SPREAD_LOW);\n            coeffs->spread_hi [g] = pow(10.0, -(coeffs->barks[g+1] - coeffs->barks[g]) * PSY_3GPP_SPREAD_HI);\n        }\n        start = 0;\n        for (g = 0; g < ctx->num_bands[j]; g++) {\n            minscale = ath(ctx->avctx->sample_rate * start / 1024.0, ATH_ADD);\n            for (i = 1; i < ctx->bands[j][g]; i++)\n                minscale = FFMIN(minscale, ath(ctx->avctx->sample_rate * (start + i) / 1024.0 / 2.0, ATH_ADD));\n            coeffs->ath[g] = minscale - minath;\n            start += ctx->bands[j][g];\n        }\n    }\n    pctx->ch = av_mallocz(sizeof(Psy3gppChannel) * ctx->avctx->channels);\n    return 0;\n}']